Source: https://patents.google.com/patent/JP2015005172A/en
Timestamp: 2019-10-20 22:12:17
Document Index: 289854028

Matched Legal Cases: ['art 114', 'art 114', 'art 118', 'art 104', 'art 106', 'art 108', 'art 110', 'art 112', 'art 114', 'art 116']

JP2015005172A - Information processing device, information processing system, and storage medium storing program - Google Patents
Information processing device, information processing system, and storage medium storing program Download PDF
JP2015005172A
JP2015005172A JP2013130504A JP2013130504A JP2015005172A JP 2015005172 A JP2015005172 A JP 2015005172A JP 2013130504 A JP2013130504 A JP 2013130504A JP 2013130504 A JP2013130504 A JP 2013130504A JP 2015005172 A JP2015005172 A JP 2015005172A
JP2013130504A
達人 佐藤
2013-06-21 Application filed by ソニー株式会社, Sony Corp filed Critical ソニー株式会社
2013-06-21 Priority to JP2013130504A priority Critical patent/JP2015005172A/en
2015-01-08 Publication of JP2015005172A publication Critical patent/JP2015005172A/en
An information processing apparatus, an information processing system, and a storage medium storing a program capable of simplifying the operation of setting identification information for an object in an image are proposed.
An image acquisition unit that acquires an image including a plurality of objects, and the second object associated with a second object by receiving an operation on the first object among the plurality of objects. An information processing apparatus comprising: a display control unit that controls identification information indicating an object.
The present disclosure relates to an information processing apparatus, an information processing system, and a storage medium storing a program.
There is a technique for recognizing an object captured in an image by using an image analysis technique. By using such a technique, for example, by inputting an image of a desired object, the object is recognized from the image, and the recognized object is identified. There is an information processing system that can acquire information on the information.
JP 2004-118562 A
On the other hand, some objects have similar appearances, and it may be difficult to uniquely identify the object only by image analysis. In this way, in consideration of the case where it is difficult to uniquely identify an object by image analysis, for example, the user can set identification information indicating the object for the object in the image. There is also an information processing system. In such an information processing system, it is also possible to acquire information associated with the identification information as information related to the target object in response to the setting of identification information by the user.
However, the task of setting identification information for an object in an image is complicated for the user, and the burden on the user increases as the number of images to be targeted increases.
Therefore, in the present disclosure, a new and improved information processing apparatus, information processing system, and storage storing a program capable of simplifying the operation of setting identification information for an object in an image Suggest a medium.
According to the present disclosure, the image acquisition unit that acquires an image including a plurality of objects, and the second associated with the second object upon receiving an operation on the first object among the plurality of objects. A display control unit that controls identification information indicating the target object.
In addition, according to the present disclosure, it is provided with a server including a terminal including a display unit, an image acquisition unit that acquires an image including a plurality of objects, and a display control unit that displays the image on the display unit. The display control unit receives an operation on the first object among the plurality of objects, and controls identification information indicating the second object associated with the second object. Is provided.
According to the present disclosure, the computer acquires an image including a plurality of objects and receives an operation on the first object among the plurality of objects, and is associated with the second object. There is provided a storage medium storing a program for executing identification information indicating the second object.
As described above, according to the present disclosure, a new and improved information processing apparatus, information processing system, and program capable of simplifying the task of setting identification information for an object in an image Is provided.
It is a figure showing a schematic system configuration of an information processing system concerning a 1st embodiment of this indication. It is a figure for demonstrating an example of operation which sets identification information with respect to the target object in an image. It is a figure for demonstrating an example of operation which sets identification information with respect to the target object in an image. It is a figure for demonstrating an example of operation which sets identification information with respect to the target object in an image. FIG. 10 is a diagram for describing an example of an operation for setting identification information for an object in an image in the information processing system according to the first embodiment of the present disclosure. FIG. 10 is a diagram for describing an example of an operation for setting identification information for an object in an image in the information processing system according to the first embodiment of the present disclosure. FIG. 10 is a diagram for describing an example of an operation for setting identification information for an object in an image in the information processing system according to the first embodiment of the present disclosure. It is the block diagram which showed the structure of the information processing apparatus and user terminal which concern on the embodiment. It is a figure for demonstrating an example of the extraction method of the target object in an image. It is a figure for demonstrating an example of the extraction method of the target object in an image. It is a figure for demonstrating an example of the data which show the area | region set in the image. It is a figure for demonstrating an example of the data which show the area | region set in the image. It is an example of the detection target detected from the image. It is a figure for demonstrating an example of the data which show the precision of the identification result of the target object in an image. It is a figure for demonstrating the process which the information processing apparatus which concerns on the embodiment estimates the identification information which shows the target object in an image. It is a figure for demonstrating the process which the information processing apparatus which concerns on the embodiment estimates the identification information which shows the target object in an image. It is a figure for demonstrating the process which the information processing apparatus which concerns on the embodiment estimates the identification information which shows the target object in an image. It is a figure for demonstrating the application example of the information processing system which concerns on the embodiment. It is a figure for demonstrating the application example of the information processing system which concerns on the embodiment. It is the flowchart which showed operation | movement of the information processing apparatus which concerns on the same embodiment. It is the flowchart which showed operation | movement of the information processing apparatus which concerns on the same embodiment. It is the flowchart which showed operation | movement of the information processing apparatus which concerns on the same embodiment. FIG. 10 is a diagram for explaining an example of a region designation method according to the first embodiment. FIG. 10 is a diagram for explaining an example of a region designation method according to the first embodiment. FIG. 10 is a diagram for explaining an example of a region designation method according to the first embodiment. FIG. 10 is a diagram for explaining an example of a region designation method according to the first embodiment. FIG. 10 is a diagram for explaining an example of a region designation method according to the first embodiment. FIG. 10 is a diagram for explaining an example of a region designation method according to the second embodiment. FIG. 10 is a diagram for explaining an example of a region designation method according to the second embodiment. FIG. 10 is a diagram for explaining an example of a region designation method according to the second embodiment. FIG. 10 is a diagram for explaining an example of a region designation method according to the second embodiment. FIG. 10 is a diagram for explaining an example of a region designation method according to the second embodiment. FIG. 10 is a diagram for explaining an example of a region designation method according to the second embodiment. FIG. 10 is a diagram for explaining an example of a region designation method according to the second embodiment. FIG. 10 is a diagram for explaining an example of a region designation method according to the second embodiment. FIG. 10 is a diagram for explaining an example of a region designation method according to the second embodiment. FIG. 10 is a diagram for explaining an example of a region designation method according to the second embodiment. FIG. 10 is a diagram for explaining an example of a region designation method according to the second embodiment. FIG. 10 is a diagram for explaining an example of a region designation method according to the second embodiment. FIG. 10 is a diagram for explaining an example of a region designation method according to the second embodiment. FIG. 10 is a diagram for explaining an example of a display mode according to the third embodiment. FIG. 10 is a diagram for explaining an example of a display mode according to the third embodiment. FIG. 10 is a diagram for explaining an example of a display mode according to the third embodiment. It is a figure for demonstrating the outline | summary of the information processing system which concerns on 2nd Embodiment of this indication. It is the figure which showed an example of the display mode which changes with proficiency. It is the flowchart which showed the operation | movement of the information processing system which concerns on the same embodiment. It is an example of the hardware constitutions of the information processing apparatus which concerns on embodiment of this indication.
1. 1. First embodiment 1.1. Outline 1.2. Configuration 1.3. Processing 1.4. Example 1: Method for specifying area (pointer operation)
1.5. Example 2: Method for designating an area (touch panel)
1.6. Example 3: Display method of identification information 1.7. Summary 2. Second Embodiment 2.1. Outline 2.2. Processing Hardware configuration
First, an overview of the information processing system 1 according to the first embodiment of the present disclosure will be described with reference to FIG. FIG. 1 is a diagram illustrating a schematic system configuration of an information processing system 1 according to the first embodiment of the present disclosure. As illustrated in FIG. 1, the information processing system 1 according to the present embodiment includes an information processing apparatus 10 and a user terminal 20. The information processing apparatus 10 and the user terminal 20 are connected via a network n0. Examples of the network n0 include the Internet, a dedicated line, a LAN (Local Area Network), and a WAN (Wide Area Network). Thus, the form of the network n0 is not limited as long as it is a network that connects different devices.
As illustrated in FIG. 1, in the information processing system 1 according to the present embodiment, the user U1 captures an image of a desired object using an imaging unit provided in the user terminal 20 such as a smartphone, for example. It transmits to the information processing apparatus 10. When the information processing apparatus 10 receives an image from the user terminal 20, the information processing apparatus 10 recognizes and identifies the object captured in the image, and presents the identification result of the object to the user U1. As a specific example, the information processing apparatus 10 performs an image analysis process on the acquired image, thereby specifying the region f1 of the target object in the image, and identifying the target object in the specified region f1. The result is presented as identification information m1 such as a label in association with the region f1.
On the other hand, there are objects that are similar in appearance among objects captured in the image, and the information processing system 1 may be difficult to uniquely identify the object in the image by image analysis processing. In some cases, it is difficult to recognize an object in an image. Therefore, the information processing system 1 provides a U / I (user interface) for designating the region f1 corresponding to the object in the image and the identification information m1 indicating the object.
However, the operation of setting identification information for an object in an image is complicated for the user U1. Therefore, hereinafter, with reference to FIGS. 2, 3A, and 3B, an example of an operation when setting identification information for an object in an image in an information processing system according to a comparative example will be described. However, the problems of the information processing system 1 according to this embodiment will be summarized. Then, the outline | summary of the information processing system 1 which concerns on this embodiment is demonstrated, referring FIG.4, FIG.5A and FIG.5B.
In the following, in describing the information processing system 1 according to the present embodiment, the information processing system 1 recognizes a dish captured in an image and presents a label indicating the recognized dish as identification information. It will be described as being. Needless to say, if an object captured in an image is recognized and the recognized object can be identified, the object is not limited to cooking.
First, referring to FIG. FIG. 2 is a diagram for explaining an example of an operation for setting identification information for an object in an image, and shows an example of an operation when a plurality of candidates are extracted as a recognition result of the object. Yes.
First, the image p111 is referred to. In the image p111, four dishes are picked up as objects, and among these, three dishes are recognized, and areas f11 to f13 indicating the respective dishes are set.
The area f11 is associated with identification information m11 indicating “Goya Chample” as identification information of the corresponding dish. On the other hand, the object corresponding to the area f12 is not uniquely specified, and a plurality of candidates indicating “Oolong tea” and “Miscellaneous rice” are shown in the area f12 as the recognition result candidates indicating the object. Identification information m121 is associated. Similarly, the object corresponding to the region f12 is not uniquely identified, and identification information m131 indicating a plurality of candidates indicating “miso soup” and “soup” is associated with the region f12.
Here, the user U1 operates, for example, the cursor c1 on the image p111 in FIG. 2, and as identification information indicating the object in the region f12 from among the candidates indicated as the identification information m121, It is assumed that “Miscellaneous rice” is selected. The image p112 shows a state in which “Miscellaneous rice” is selected from the candidates indicated as the identification information m121 in the image p111. In this case, the information processing system according to the comparative example receives the selection by the user U1, recognizes that the object indicated by the region f12 is “Miscellaneous rice”, and as shown in the image p112, the region f12 Is associated with identification information m122 indicating "milled rice".
Next, the image p112 is referred to. Here, for example, the user U1 selects “miso soup” as identification information indicating the object indicated by the region f13 from among the candidates indicated as the identification information m131 by operating the cursor c1. And The image p113 shows a state where “miso soup” is selected from the candidates indicated as the identification information m131 in the image p112. In this case, the information processing system according to the comparative example receives the selection by the user U1, recognizes that the object indicated in the region f13 is “miso soup”, and displays the information in the region f13 as illustrated in the image p113. The identification information m132 indicating “miso soup” is associated with the information.
As described above, identification information m122 and m132 indicating the object imaged in the area are set for each of the areas f12 and f13 in which the object is not uniquely specified.
Reference is now made to FIG. FIG. 3A is a diagram for explaining an example of an operation for setting identification information for an object in an image. An area corresponding to an unrecognized object is designated and identified for the object. An example of an operation for setting information is shown.
First, the image p114 is referred to. In the image p114, identification information items m11, m122, and m132 that uniquely specify the object included in each region are associated with the regions f11, f12, and f13, respectively. On the other hand, one point is not recognized among the four picked-up dishes. Therefore, hereinafter, in the information processing system according to the comparative example, an unrecognized dish is set as an object, an area f14 indicating the object is set in the image p114, and identification information for the area f14 is set. The procedure for setting is described.
For example, the user U1 designates a region of the object that is not recognized by operating the cursor c1. In response to the designation of this area, the information processing system according to the comparative example sets the designated area as the area f14. When the region f14 is set, the information processing system according to the comparative example attempts to identify an object included in the region f14 by performing image analysis processing on the region f14 in the image p114. Here, the information processing system according to the comparative example will be described assuming that the object in the region f14 cannot be identified.
Next, the image p115 is referred to. The image p115 shows a case where an area f14 is set and it is difficult to identify an object included in the area f14. In the image p115, identification information m141 indicating that the object cannot be specified is set for the region f14. In this case, the information processing system according to the comparative example displays the setting screen v115 for setting the identification information in response to the designation for the identification information m141 from the user U1.
Reference is now made to FIG. 3B. FIG. 3B is a diagram for describing an example of an operation for setting identification information for an object in an image, and is a diagram focusing on p115 in FIG. 3A. As shown in FIG. 3B, the setting screen v115 may include an input field for the user to input identification information, or the candidate selected from the list is identified by presenting the list of candidates. U / I for setting as information may be included.
Here, the image p116 of FIG. 3A is referred. The image p116 shows a state in which “hijiki” is set as the identification information of the object in the region f14 in the region f14 based on the operation via the setting screen v115 in the image p115. In this case, the information processing system according to the comparative example receives the input of the identification information by the user U1, recognizes that the object indicated by the region f14 is “hijiki”, and as illustrated in the image p116, The identification information m142 indicating “hijiki” is associated with the area f14.
As described above, in the information processing system according to the comparative example, when it is difficult to uniquely identify an object captured in an image or when it is difficult to recognize the object in the image, the user U1 However, it is necessary to individually set identification information for the area corresponding to each object, and the procedure is complicated.
Therefore, in the information processing system 1 according to the present embodiment, when identification information indicating the one object is specified for one object in the image, the probability of co-occurring with the one object is determined. Estimate other high objects. By estimating other objects in this way, the information processing system 1 according to the present embodiment omits or simplifies the operation related to setting identification information for the other objects, and corresponds to each object. The procedure for setting identification information for a region is simplified. Hereinafter, the “probability of co-occurrence” may be referred to as “co-occurrence probability”.
Here, the co-occurrence probability will be described. The co-occurrence probability indicates a probability that another object co-occurs with one object, for example, a probability that two objects exist together. Further, the information processing system 1 may recognize the co-occurrence probability as, for example, a probability that another object is imaged together when one object is imaged in the image. The “co-occurrence” in this case is not limited to “co-occurrence” in the field of so-called natural language processing. As a specific example, when the object is a dish, the “co-occurrence probability” is, for example, based on compatibility between dishes such as good or bad, and other dishes together with one dish It may indicate the possibility of being selected or not selected. In other words, as a series of dishes provided in one meal, the probability that another dish is imaged in the image together with the one dish is shown. Needless to say, this is not limited to cooking. For example, when the object is a person, the co-occurrence probability may indicate a probability that another person is captured in the image together with the one person. Such a co-occurrence probability between one object and another object may be set based on the relationship between these objects or a predetermined logic, or an image acquired in the past is analyzed. You may ask for it empirically.
Next, referring to FIGS. 4, 5A, and 5B, the outline of the information processing system 1 according to the present embodiment will be described in the same manner as in the description based on FIGS. 2, 3A, and 3B. An example in which identification information is set for a dish imaged in FIG.
First, referring to FIG. FIG. 4 is a diagram for explaining an example of an operation for setting identification information for an object in an image in the information processing system 1 according to the present embodiment. Note that an image p121 in FIG. 4 shows a state similar to that of the image p111 shown in FIG.
For example, the user U1 operates “cursor c1” to select “Miscellaneous rice” as the identification information indicating the object indicated by the region f12 from the candidates indicated as the identification information m121. . The information processing system 1 receives the selection by the user U1, recognizes that the object indicated by the region f12 is “Miscellaneous rice”, and displays “Miscellaneous rice” for the region f12 as shown in the image p122. The identification information m122 to be shown is associated.
In addition, the information processing system 1 determines that the object corresponding to the region f12 is “Miscellaneous rice”, and therefore, between “Misou soup” and “Soup” indicated as the identification information m131, Get the co-occurrence probability of. Then, the information processing system 1 compares the acquired co-occurrence probabilities corresponding to “miso soup” and “soup”, and specifies (or estimates) the identification information of the object corresponding to the region f13 based on the comparison result. . Here, the co-occurrence probability with “Miscellaneous rice” is higher in “Miso soup” than in “Soup”. In this case, the information processing system 1 recognizes that the object indicated in the region f13 is “miso soup” having a high co-occurrence probability with “Miscellaneous rice” among “miso soup” and “soup”. Then, identification information m132 indicating “miso soup” is associated with the region f13.
As described above, the information processing system 1 according to the present embodiment receives the designation of the identification information m122 indicating the object corresponding to the region f12 and determines the co-occurrence probability with the object indicated by the specified identification information. Based on this, the object corresponding to the region f13 is estimated. Then, the information processing system 1 sets the identification information m132 for the region f13 based on the estimation result. With such a configuration, in the information processing system 1 according to the present embodiment, compared to the information processing system according to the comparative example described above (see FIG. 2), an operation related to setting identification information for the region f13 may be omitted. It becomes possible.
Reference is now made to FIG. FIG. 5A is a diagram for describing an example of an operation for setting identification information for an object in an image in the information processing system 1 according to the present embodiment. Note that the image p123 in FIG. 5A shows a state similar to that of the image p114 shown in FIG.
For example, the user U1 designates a region of the object that is not recognized by operating the cursor c1. In response to the designation of this area, the information processing system 1 according to the present embodiment sets the designated area as the area f14. After setting the region f14, the information processing system 1 tries to identify the target object included in the region f14 by performing image analysis processing on the region f14 in the image p123. Here, it is assumed that the information processing system 1 has failed to identify the object in the region f14.
Next, the information processing system 1 estimates other objects having a high co-occurrence probability with objects corresponding to the regions f11, f12, and f13. In the example illustrated in FIG. 5A, the information processing system 1 includes “Goya Chample” indicated by the identification information m11, “Miscellaneous rice” indicated by the identification information m122, and “Miso soup” indicated by the identification information m132. "Hijiki" is identified as an object having a high co-occurrence probability between That is, the information processing system 1 recognizes that the object indicated by the region f14 is “hijiki”, and associates the identification information m142 indicating “hijiki” with the region f14 as shown in the image p124.
Further, when the region f14 is set, the object in the region f14 may be identified as “hijiki”, and the objects in other regions may be recognized based on the identification result of the object. For example, FIG. 5B is a diagram for describing an example of an operation for setting identification information for an object in an image in the information processing system 1 according to the present embodiment. The example in which the target object in the area | region of is identified is shown.
An image p125 in FIG. 5B shows a case where the user U1 designates an unrecognized “hijiki” region by operating the cursor c1 in the same state as the image p121 in FIG. As shown in FIG. 5B, it is assumed that the information processing system 1 cannot uniquely identify the objects in the regions f12 and f13 at this time.
In response to the designation of the area by the user U1, the information processing system 1 sets the designated area as the area f14, and tries to identify the object included in the area f14. At this time, it is assumed that the information processing system 1 recognizes that the object in the region f14 is “hijiki”. In this case, as illustrated in the image p126, the information processing system 1 associates the identification information m142 indicating “hijiki” with the region f14.
In addition, the information processing system 1 identifies the objects in the regions f12 and f13 based on the co-occurrence probability between the “hijiki” newly identified as the objects in the region. In the example shown in the image p126, the information processing system 1 identifies “Miscellaneous rice” having a high co-occurrence probability with “Hijiki” among “Oolong tea” and “Miscellaneous rice” indicated in the identification information m121. , Identification information m122 indicating “milled rice” is displayed in association with the region f12. Similarly, the information processing system 1 specifies “miso soup” having a high co-occurrence probability with “hijiki” among “miso soup” and “soup” indicated in the identification information m131, and indicates “miso soup”. The identification information m132 is displayed in association with the area f13.
Thus, even when the information processing system 1 according to the present embodiment cannot recognize the object corresponding to the region f14 based on the image analysis processing, the co-occurrence probability with the object indicated by the other region Based on the above, the object corresponding to the region f14 is estimated. Then, the information processing system 1 sets the identification information m142 for the region f14 based on the estimation result. With such a configuration, in the information processing system 1 according to the present embodiment, compared to the information processing system according to the comparative example described above (see FIG. 3), an operation related to setting identification information for the region f14 may be omitted. It becomes possible.
[1.2. Constitution]
Next, the configuration of the information processing apparatus 10 and the user terminal 20 according to the present embodiment will be described with reference to FIG. FIG. 6 is a block diagram illustrating configurations of the information processing apparatus 10 and the user terminal 20 according to the present embodiment. As illustrated in FIG. 6, the information processing apparatus 10 includes an image acquisition unit 102, an object extraction unit 104, an object specification unit 106, an accuracy specification unit 108, a feature amount data storage unit 110, and identification information specification. Unit 112, extraction unit 114, co-occurrence probability data storage unit 116, estimation unit 118, display control unit 120, and update unit 122. The user terminal 20 includes an imaging unit 202, an operation unit 204, and a display unit 206.
In the following, each configuration of the information processing apparatus 10 and the user terminal 20 is divided into “processing based on image acquisition”, “processing based on area setting”, and “processing based on identification information setting”. Description will be made by paying attention to the configuration that operates at an opportunity.
(Processing based on image acquisition)
First, as “processing based on image acquisition”, a configuration in which the information processing apparatus 10 operates during the processing will be described, focusing on processing when the image captured by the imaging unit 202 is acquired. Specifically, the information processing apparatus 10 recognizes the object in the image acquired from the imaging unit 202, and based on the recognition result, the area corresponding to the recognized object and the identification information of the object are obtained by the user. Present to U1.
The imaging unit 202 captures an image of one or more objects. The image captured at this time may be an image like a still image. A specific example of the imaging unit 202 is a camera provided in the user terminal 20 such as a smartphone. Although FIG. 6 shows an example in which the user terminal 20 is provided with the imaging unit 202, the imaging unit 202 is not necessarily a part of the user terminal 20. For example, the imaging unit 202 may be provided independently of the user terminal 20.
The image acquisition unit 102 acquires the image captured by the imaging unit 202. The image acquisition unit 102 outputs the acquired image to the object extraction unit 104.
The object extraction unit 104 acquires an image from the image acquisition unit 102. The target object extraction unit 104 extracts a region formed with a specific shape included in the acquired image or a region where a characteristic color component or texture exists as a target region. Hereinafter, an example of a method for extracting a region of an object from an image will be described with reference to FIGS.
First, referring to FIG. FIG. 7 is a diagram for explaining an example of a method for extracting an object in an image, and shows an example in which a circle or an ellipse area included in an image is extracted as an object area. In many cases, dishes are served in containers such as dishes. Therefore, for example, when an area corresponding to a circular or elliptical container in an image is extracted, the extracted area often includes a dish that is an object. Therefore, for example, the object extraction unit 104 recognizes the extracted area as an area including the dish that is the object by extracting a circular or elliptical area corresponding to the container from the image. it can. Hereinafter, the circular shape and the elliptical shape are simply referred to as an “elliptical shape”. Therefore, the term “elliptical” includes “circular”.
For example, the image p131 includes four images of dishes on an oval dish. For example, the object extraction unit 104 performs image analysis processing on the image p131 to extract edges in the image p131 and extract elliptical regions e21 to e24 formed by the extracted edges. Then, as shown in the image p132, the object extraction unit 104 recognizes the extracted elliptical areas e21 to e24 as cooking areas f21 to f24.
In the image p132, the object extraction unit 104 sets a region circumscribing the elliptical regions e21 to e24 with the vertical direction of the image p132 as the vertical side and the horizontal direction of the image p132 as the horizontal side. It is set as f21 to f24. Note that the shapes of the regions f21 to f24 illustrated in FIG. 7 are merely examples, and the shape of the regions f21 to f24 is not limited as long as the cooking region that is the object can be set. For example, the object extraction unit 104 may set the extracted elliptical areas e21 to e24 as the cooking areas f21 to f24 as they are.
Reference is now made to FIG. FIG. 8 is a diagram for explaining an example of a method for extracting an object in an image, and illustrates an example in which a rectangular area included in an image is extracted as an object area.
For example, in the image p141, one point of food on a square dish is captured. For example, the object extraction unit 104 performs image analysis processing on the image p141, thereby extracting an edge in the image p141 and extracting a quadrangular region e31 formed by the extracted edge. Then, as shown in the image p142, the object extraction unit 104 recognizes the extracted rectangular area e31 as a cooking area f31.
In the image p142, similarly to the image p132 in FIG. 7, a region that circumscribes the square region e31 with the vertical direction of the image p142 being the vertical side and the horizontal direction of the image p142 being the horizontal side is the cooking region f31. It is set as.
Next, an example of a method for defining data indicating a region set in an image will be described with reference to FIGS. 9 and 10. First, FIG. 9 will be referred to. FIG. 9 is a diagram for explaining an example of data indicating a region set in an image. The vertical direction of the image p1 set in the image p1 is a vertical side, and the horizontal direction is a horizontal side. It is a figure for demonstrating an example of the data which show the area | region f1 made into.
In FIG. 9, the reference symbol h0 indicates the height (length in the vertical direction) of the image p1. Reference sign w0 indicates the width (length in the horizontal direction) of the image p1. Reference numeral n0 indicates a reference vertex among the vertices of the rectangular region f1. In the example shown in FIG. 9, the upper left vertex of the square region f1 is set as a reference vertex n0. Reference sign h1 indicates the height of the region f1, and reference sign w1 indicates the width of the region f1.
Reference is now made to FIG. FIG. 10 is a diagram for explaining an example of data indicating an area set in an image, and shows an example of a definition of data indicating an area. Hereinafter, data indicating a region may be referred to as “region data”. In the example shown in FIG. 10, the name d111, the type d112, and the value range d113 are shown as the definition (for example, data type) of the area data d11.
The name d111 indicates the type of data. As indicated by the name d111, the area data d11 can be defined by data indicating “area position” and data indicating “area size”.
The type d112 indicates the type of information included in each of the data indicating “area position” and the data indicating “area size”, and the value range d113 indicates the information indicated by the type d112. Indicates the range of values that can be taken.
For example, the data indicating the “region position” indicates the position of the region f1 in the image p1 in FIG. 9, and corresponds to the position of the vertex n0. In the example shown in FIG. 10, the position of the vertex n0 is shown as a relative value with respect to the height h0 and the width w0 of the image p1. As a specific example, the horizontal position “x” of the vertex n0 in the data indicating “region position” is “0.0” for the left end of the image p1 shown in FIG. 9 and “1.0” for the right end. The position in the horizontal direction is shown. Similarly, the vertical position “y” of the vertex n0 indicates the vertical position when the upper end of the image p1 shown in FIG. 9 is “0.0” and the lower end is “1.0”.
Further, the data indicating “area size” indicates the size of the area f1 in the image p1 in FIG. In the example shown in FIG. 10, the size of the region f1 is shown as a relative value with respect to the height h0 and the width w0 of the image p1. As a specific example, the width “width” of the region f1 in the data indicating the “region size” is the relative width of the region f1 when the width w0 of the image p1 is “1.0”. And is derived by w1 / w0. Similarly, the height “height” of the region f1 indicates the relative height of the region f1 when the height h0 of the image p1 is “1.0”, and is derived by h1 / h0. .
Note that the examples shown in FIGS. 9 and 10 are merely examples, and the format of the data indicating the area is not limited as long as the area in the image can be specified.
As described above, the object extraction unit 104 extracts the area indicating the object from the acquired image, and generates area data indicating the area for each extracted area. The object extraction unit 104 outputs the acquired image and region data corresponding to each region extracted from the image to the accuracy specifying unit 108.
The accuracy specifying unit 108 acquires an image and region data corresponding to the region extracted from the image from the object extraction unit 104. The accuracy specifying unit 108 specifies each area set in the acquired image based on the acquired area data.
After specifying the region in the image, the accuracy specifying unit 108 identifies the object included in the specified region. As a specific example, the accuracy specifying unit 108 compares the feature amount determined from each region in the image with the feature amount stored in advance for each detection target, thereby determining the accuracy of the target object included in the region. Is calculated for each detection target. Data indicating the feature amount for each detection target is stored in the feature amount data storage unit 110. The “accuracy” is an index indicating the certainty that the object in the image is the detection target. This accuracy can be defined, for example, based on the degree to which the feature quantity of the object extracted from the image matches the feature quantity of the detection target. Hereinafter, information indicating the accuracy of the object calculated for each detection object may be referred to as “identification score d13”.
Note that the feature amount data storage unit 110 may also store sample data (for example, an image for each detection target) for deriving data indicating the feature amount for each detection target. Reference is now made to FIG. FIG. 11 illustrates an example of a detection target to be detected from an image, and illustrates an example in the case of detecting a dish as an object. In the example shown in FIG. 11, the detection target is shown as class d12.
Reference is now made to FIG. FIG. 12 is a diagram for explaining an example of data indicating the accuracy of the identification result of the object in the image, that is, an example of the identification score d13. In the example shown in FIG. 12, the name d131, the class d12, and the range d132 are shown as the definition (for example, data type) of the identification score d13.
The name d131 indicates the type of the identification score d13. For example, the example illustrated in FIG. 12 illustrates an example of the identification score d13 in which cooking is detected. Therefore, a “dish attribute” indicating that the identification score d13 is an identification score with dish as a detection target is set in the name d131.
The class d12 corresponds to the class d12 shown in FIG. 11, and indicates a range in which a value indicating the accuracy of the detection target indicated by the range d132 and the class d12 can be obtained. In the example illustrated in FIG. 12, the range of values that can be taken as the accuracy of the detection target indicated by the class d12 is set from “−1.0” to “1.0”. When the value indicating the accuracy exceeds 0, it indicates the certainty that the object in the image is the detection target indicated by the class d12. When the value indicating the accuracy is less than 0, the detection target indicated by the class d12 is indicated. Indicates uncertainty. In other words, the higher the value indicating the accuracy and the higher the value, the higher the probability that the object in the image is the detection target indicated by the class d12. Similarly, when the value indicating the accuracy is low, the probability that the object in the image is the detection target indicated by the class d12 is low, and when the value indicating the accuracy is less than 0, the detection target is another detection target. The probability is higher.
The accuracy below 0 can be defined, for example, by accumulating data indicating that the object in the image is different from the object indicated as the detection object as sample data.
As described above, the accuracy specifying unit 108 calculates the identification score d13 indicating the accuracy for each detection target indicated by the class d12 for each target included in each region in the image. For example, the identification score d13 may be calculated as information indicating the probability that an object included in each region in the image is each detection target indicated by the class d12. Further, the identification score d13 corresponds to an example of “accuracy information”.
The accuracy specifying unit 108 outputs the acquired image, the region data corresponding to the region extracted from the image, and the identification score d13 calculated for each object in the region to the extracting unit 114.
The extraction unit 114 acquires the image, the region data corresponding to the region extracted from the image, and the identification score d13 calculated for each object in the region from the accuracy specifying unit 108.
For each target, the extraction unit 114 refers to the identification score d13 corresponding to each detection target indicated by the class d12, and specifies a detection target for which the identification score d13 indicates a value equal to or greater than a predetermined threshold.
As a specific example, the threshold value is set to “0.8”, and among each detection target indicated by the class d12 for one target, the identification score d13 of “fish dish” is “0.8” or more. In such a case, the extraction unit 114 specifies “fish dish” based on the identification score d13 of the one object.
When the detection target whose identification score d13 is equal to or greater than the threshold value can be specified, the extraction unit 114 extracts the co-occurrence probability data d14 corresponding to the specified detection target from the co-occurrence probability data storage unit 116. The co-occurrence probability data storage unit 116 is a storage unit for storing the co-occurrence probability data d14.
Here, the co-occurrence probability data d14 will be described. The co-occurrence probability data d14 is data indicating the probability that one object and each detection target indicated by the class d12 co-occur when one object is specified. For example, the co-occurrence probability data d14 corresponding to “fish dish” indicates the probability that “fish dish” and each dish to be detected indicated in the class d12 co-occur for each dish to be detected. It is the data shown. The co-occurrence probability data storage unit 116 stores co-occurrence probability data d14 for each detection target indicated by the class d12. In other words, the co-occurrence probability data storage unit 116 manages and stores information indicating the co-occurrence probability for each combination of detection targets included in the class d12. The value range of the information indicating the co-occurrence probability is “0.0” to “1.0”. For example, in the case of the co-occurrence probability data d14 of “fish dish”, the detection target with higher information indicating the co-occurrence probability indicates that the probability of co-occurring with “fish dish” is higher.
When the “fish dish” is specified as the detection target having the identification score d13 equal to or greater than the threshold, the extraction unit 114 extracts the co-occurrence probability data d14 corresponding to “fish dish” from the co-occurrence probability data storage unit 116. .
Moreover, the extraction part 114 may identify the target object with which the said identification score d13 was matched as the specified detection target, when the detection target whose identification score d13 is more than a threshold value can be specified. In this case, the extraction unit 114 associates the identification information indicating the specified detection target, that is, the identification information indicating the identification result of the target object, with the area data of the area corresponding to the target object. This threshold value is also a threshold value of the identification score d13 for the extraction unit 114 to identify the object.
Reference is now made to FIG. FIG. 13 is a diagram for describing processing in which the information processing apparatus 10 according to the present embodiment estimates identification information indicating an object in an image. For example, it is assumed that the extraction unit 114 acquires the image p151 illustrated in FIG. 13, region data indicating the regions f41 and f42 in the image, and the identification score d13 calculated for each object in the region.
In the example illustrated in FIG. 13, it is assumed that the object in the region f41 has an identification score d13 corresponding to “fish dish” equal to or greater than a threshold value. In this case, the extraction unit 114 extracts the co-occurrence probability data d14a corresponding to “fish dish” from the co-occurrence probability data storage unit 116. Further, the extraction unit 114 associates identification information indicating “fish dish” with the area data corresponding to the area f41.
The identification score d13b indicates the identification score of the object in the region f42. In addition, about the target object in the area | region f42, the extraction part 114 shall be able to specify the detection target whose identification score d13b is more than a threshold value.
The extracting unit 114 estimates the acquired image, the region data corresponding to the region extracted from the image, the identification score d13 for each object in the region, and the extracted co-occurrence probability data d14. Output to. Needless to say, if the co-occurrence probability data d14 cannot be extracted, the extraction unit 114 does not output the co-occurrence probability data d14 to the estimation unit 118. In the example shown in FIG. 13, the object in the region f41 is identified as “fish dish”. Therefore, in this case, the extraction unit 114 does not necessarily need to output the identification score d13 to the estimation unit 118 for the object in the region f41.
The estimation unit 118 acquires the image, the region data corresponding to the region extracted from the image, the identification score d13 for each object in the region, and the extracted co-occurrence probability data d14 from the extraction unit 114. .
The estimation unit 118 confirms whether or not the identification information indicating the identification result of the object in the area indicated by the area data is associated with each acquired area data, and the area where the identification information is not associated Extract data.
For example, in the example shown in FIG. 13, the identification information m31 is associated with the area data corresponding to the area f41, and the identification information is not associated with the area data corresponding to the area f42. In this case, the estimation unit 118 extracts region data corresponding to the region f42.
The estimation unit 118 calculates a new identification score d13 'by multiplying the extracted identification data d14 by the acquired co-occurrence probability data d14 for each of the extracted region data.
For example, in the case of the example shown in FIG. 13, when focusing on the object corresponding to the region f42, the identification score d13b of “soup” is “0.6”. The identification result of the object corresponding to the region f41, that is, the co-occurrence probability that “fish dish” and “soup” co-occur is “0.32.” Therefore, when the new identification score of the object corresponding to the region f42 is the identification score d13b ', the identification score d13b' corresponding to "Soup" is "0.192".
On the other hand, the identification score d13b of “miso soup” in the object corresponding to the region f42 is “0.8”. The co-occurrence probability that “fish dishes” and “miso soup” co-occur is “0.81”. Therefore, the identification score d13b ′ corresponding to “miso soup” is “0.648”.
From such a result, for example, when referring to the identification score d13b of the object corresponding to the region f42, “Soup” is “0.6” and “Miso Soup” is “0.8”. Therefore, it is difficult to determine whether the object corresponding to the region f42 is “soup” or “miso soup” only with the identification score d13b. On the other hand, the identification score d13b ′ calculated based on the identification score d13b and the co-occurrence probability of “fish dishes” is “0.192” for “soup” and “0.648” for “miso soup”. is there. From this, compared with the case where the determination is made based only on the identification score d13b, in the determination based on the identification score d13b ′, the accuracy that the object corresponding to the region f42 is “soup” and the accuracy that it is “miso soup” are more dissimilar. In addition, it becomes easier to identify the object.
As described above, when the identification score d13 ′ is calculated for each of the objects corresponding to the extracted region data, the estimation unit 118 first determines the identification score d13 ′ from each calculated identification score d13 ′. To a predetermined first threshold value. When the detection target whose identification score d13 ′ is equal to or greater than the first threshold value can be specified, the estimation unit 118 recognizes the corresponding target object as the specified detection target, and sets the region data corresponding to the target object The identification information indicating the specified detection target is associated with the identification information.
As a specific example, the first threshold value is set to “0.7”, and among the detection targets indicated by the class d12 for one target, the identification score d13 ′ of “meat” is “0. If it is 7 ”or more, the estimating unit 118 specifies“ meat dish ”based on the identification score d13 ′ of the one object. Then, the estimation unit 118 associates identification information indicating “meat” with the region data corresponding to the one target object. In the following, the first threshold value for identifying the above-described object may be referred to as “reference 1”.
When the detection target whose identification score d13 'satisfies the criterion 1 cannot be specified, the estimation unit 118 compares each identification score d13' with a second threshold value that is smaller than the first threshold value. This second threshold value is a threshold value for extracting candidates for the identification result of the object. The estimation unit 118 identifies a detection target whose identification score d13 'is equal to or greater than the second threshold, and associates identification information indicating the identified detection target with region data corresponding to the target. At this time, the estimation unit 118 may attach information indicating that the identification information is a candidate for the identification result of the object to the identification information associated with the region data.
As a specific example, the second threshold value is “0.3”, and among the detection targets indicated by the class d12 for one object, the identification scores d13 ′ of “miso soup” and “soup” are When it is “0.3” or more, the estimation unit 118 identifies “miso soup” and “soup” based on the identification score d13 ′ of the one object. Then, the estimation unit 118 associates identification information indicating “miso soup” and identification information indicating “soup” with the area data corresponding to the one target object. At this time, the estimation unit 118 attaches information indicating that the identification information is a candidate for the identification result of the object to the identification information indicating “miso soup” and the identification information indicating “soup”. In the following, the second threshold value for specifying the target identification result candidate shown above may be referred to as “reference 2”.
As described above, the estimation unit 118 determines whether or not the calculated identification score d13 'satisfies the criterion 1 or the criterion 2 for each object corresponding to the extracted region data. Based on the determination result, the estimation unit 118 associates identification information indicating the identification result of the target object or identification information indicating a candidate of the identification result with the area data of each target object. In addition, when the detection target whose identification score d13 'satisfies either the reference 1 or the reference 2 is not specified, the identification information is not associated with the corresponding region data. As described above, the estimation unit 118 estimates the identification result of the object corresponding to each area data by determining whether the criterion 1 or the criterion 2 is satisfied.
The estimation unit 118 outputs the acquired image and region data corresponding to the region extracted from the image to the display control unit 120.
The display control unit 120 acquires an image and region data corresponding to the region extracted from the image from the estimation unit 118. The display control unit 120 displays the acquired image on the display unit 206.
In addition, the display control unit 120 superimposes an area based on the acquired area data on the image displayed on the display unit 206. For example, in the example illustrated in FIG. 13, the display control unit 120 displays the image p151 on the display unit 206, and superimposes the regions f41 and f42 on the image p151 based on the acquired region data.
Next, the display control unit 120 extracts identification information associated with each area data. When the identification information can be extracted, the display control unit 120 displays the extracted identification information in association with the area corresponding to the area data. For example, in the example shown in FIG. 13, it is assumed that identification information indicating “fish dish” is associated with the area data of the area f41. In this case, the display control unit 120 displays the identification information m41 indicating “fish dish” in association with the region f41.
In addition, when the identification information indicating the candidate identification result of the object is associated with the region data, the display control unit 120 associates the extracted identification information with the region corresponding to the region data, and The identification information is displayed so as to be selectable. For example, in the example illustrated in FIG. 13, it is assumed that identification information indicating “miso soup” and identification information indicating “soup” are associated with the region data of the region f42 as candidates for the identification result. In this case, the display control unit 120 causes the region f42 to display the identification information m421 presented so that “miso soup” and “soup” can be selected in association with each other. As a result, the user U1 can select either “miso soup” or “soup” presented as the identification information m421 via the operation unit 204.
(Processing based on area settings)
Next, the operation of the information processing apparatus 10 when the user U1 newly sets an area indicating the target object for the image displayed on the display unit 206 will be described. In the following description, it is assumed that the image p151 shown in FIG. 13 is in a state before area designation, and FIG. 14 is in a state after area designation. FIG. 14 is a diagram for describing processing in which the information processing apparatus 10 according to the present embodiment estimates identification information indicating an object in an image. Specifically, the example illustrated in FIG. 14 illustrates an image p152 in which the region f43 is designated with reference to the image p151 illustrated in FIG. Therefore, hereinafter, the operation of each component of the information processing apparatus 10 will be described on the assumption that the area U43 illustrated in FIG. 14 is designated by the user U1. Further, the accuracy specifying unit 108, the extracting unit 114, and the estimating unit 118 will be described as holding each data acquired in “processing based on image acquisition”. In addition, when the data acquired in the “process based on image acquisition” is sequentially discarded, the accuracy specifying unit 108, the extraction unit 114, and the estimation unit 118 may acquire the corresponding data again as necessary. .
The display control unit 120 causes the display unit 206 to display a U / I for designating a region in the image displayed on the display unit 206. Note that the control data for displaying the U / I may be stored in a place where the display control unit 120 can read it.
The user U1 operates the U / I displayed on the display unit 206 via the operation unit 204 to specify an area in the image. Here, as shown in FIG. 14, it is assumed that the region f43 is designated in the image p152. A specific example of U / I for designating an area in an image will be described later as Examples 1 and 2.
When the region f43 is designated in the image p152 displayed on the display unit 206, the object designating unit 106 acquires information indicating the region f43 in the image p152 from the operation unit 204. Examples of the information indicating the region f43 include information indicating the position and size of the region f43 in the image p152. The object specifying unit 106 generates area data corresponding to the area f43 based on the acquired information indicating the area f43. Note that the area data generated by the object specifying unit 106 is the same as the area data generated by the object extracting unit 104 described above.
The object specifying unit 106 outputs the generated area data to the accuracy specifying unit 108. In the following description, it is assumed that the object specifying unit 106 has output the region data corresponding to the region f43 to the accuracy specifying unit 108.
The accuracy specifying unit 108 acquires area data of the area f43 from the object specifying unit 106. The accuracy specifying unit 108 specifies the region f43 in the image acquired from the object extraction unit 104 in “processing based on image acquisition” based on the acquired region data. After specifying the region f43 in the image, the accuracy specifying unit 108 calculates the identification score d13 of the object included in the region f43. The calculation method of the identification score d13 is the same as the method described above in the “processing based on image acquisition”. Hereinafter, the identification score d13 of the object corresponding to the region f43 may be referred to as “identification score d13c”.
The accuracy specifying unit 108 outputs the newly acquired region data and the identification score d13 of the object in the region to the extraction unit 114. Hereinafter, description will be made assuming that the accuracy specifying unit 108 outputs the region data of the region f43 and the identification score d13c of the object in the region f43 to the extraction unit 114.
The extraction unit 114 acquires the region data of the region f43, the identification score d13c of the object in the region f43, and the accuracy specifying unit 108.
The extraction unit 114 refers to the acquired identification score d13c and identifies a detection target in which the identification score d13c indicates a value equal to or greater than a predetermined threshold. If the detection target can be specified, the extraction unit 114 extracts the co-occurrence probability data d14 corresponding to the detection target and the detection target for the region data as described above in the “processing based on image acquisition”. Is associated with identification information indicating.
The extraction unit 114 outputs the region data of the region f43 to the estimation unit 118. In addition, when extracting the co-occurrence probability data d14 based on the identification score d13 of the object in the region f43, the extraction unit 114 outputs the extracted co-occurrence probability data d14 to the estimation unit 118. Here, it is assumed that the extraction unit 114 cannot extract the co-occurrence probability data d14 based on the identification score d13 of the object in the region f43. Therefore, it is assumed that the area data corresponding to the area f43 is not associated with identification information indicating the object in the area f43.
The estimation unit 118 acquires the region data corresponding to the region f43 and the identification score d13c of the object in the region f43 from the extraction unit 114.
The estimation unit 118 checks whether or not identification information is associated with the area data corresponding to the acquired area f43. Here, it is assumed that identification information is not associated with the area data corresponding to the area f43.
The estimation unit 118 calculates a new identification score d13 ′ by multiplying the identification score d13c of the object in the region f43 by the co-occurrence probability data d14 acquired in “processing based on image acquisition”. In the case of the example illustrated in FIG. 14, the estimation unit 118 calculates the identification score d13 ′ by multiplying the identification score d13 c by the co-occurrence probability data d14 a corresponding to the object in the region f41.
The subsequent processing for the region f43 is the same as the above-described “processing based on image acquisition”. That is, the estimation unit 118 determines the identification score d13 'corresponding to the object in the region f43 based on the criterion 1 and the criterion 2, and associates the identification information with the region data corresponding to the region f43 based on the determination result. In the following, in order to make the explanation of the operation of the information processing apparatus 10 according to the present embodiment easier to understand, a detection target that satisfies either criterion 1 or criterion 2 is specified based on the identification score d13 ′ corresponding to the region f43. Suppose that it was not done. That is, in this case, the identification information is not associated with the area data corresponding to the area f43.
The display control unit 120 acquires region data corresponding to the region f43 from the estimation unit 118, and causes the region f43 to be superimposed and displayed in the image acquired in the “processing based on image acquisition” based on the acquired region data. Note that identification information is not associated with the area data corresponding to the area f43. Therefore, in this case, identification information corresponding to the region f43 is not displayed.
As described above, with the image p151 shown in FIG. 13 as a reference, the region f43 is superimposed and displayed in the image as shown in the image p152 in FIG.
In addition, although the example which shows a new area | region in an image was demonstrated above, you may enable it to correct the area | region already displayed in the image. In this case, the object designating unit 106 may acquire the area data of the area corrected by the operation of the user U1 from the operation unit 204 and output it to the accuracy specifying unit 108. Thereafter, the accuracy specifying unit 108, the extraction unit 114, the estimation unit 118, and the display control unit 120 update the area data that has already been acquired to the newly acquired area data after correction, and the area after the update. What is necessary is just to perform the series of processes described above for the data.
The object designating unit 106 may acquire information indicating the correction target area from the operation unit 204 and output the acquired information indicating the area to the accuracy specifying unit 108. Here, the information indicating the area is information for uniquely specifying the area from the image. As a specific example of information indicating a region, identification information attached to each region data for identifying each region in the image may be used, or region data itself may be used. The above-described information indicating the region is notified to the accuracy specifying unit 108, the extraction unit 114, the estimation unit 118, and the display control unit 120, so that these configurations can be obtained from the region data before correction that has already been acquired. It becomes possible to specify the target area data from the inside.
(Processing based on identification information settings)
Next, the operation of the information processing apparatus 10 when the user U1 sets identification information for the area in the image displayed on the display unit 206 will be described. In the following description, it is assumed that the image p152 shown in FIG. 14 is in a state before setting identification information, and FIG. 15 is in a state after setting identification information. FIG. 15 is a diagram for explaining processing in which the information processing apparatus 10 according to the present embodiment estimates identification information indicating an object in an image. Specifically, the example illustrated in FIG. 15 is an image in a case where “miso soup” is selected from the candidate objects in the region f42 presented as the identification information m421 with reference to the image p152 illustrated in FIG. p153 is shown. Similar to the description of the “process based on region setting” described above, the accuracy specifying unit 108, the extracting unit 114, and the estimating unit 118 hold each data acquired in the “process based on region setting”. Explain that it is.
The user U1 operates the U / I displayed on the display unit 206 via the operation unit 204 to change the identification information indicating the object in the region f42 from the candidates presented as the identification information m421. Select the corresponding candidate. In the following description, it is assumed that “miso soup” is selected as the identification information indicating the object in the region f42.
When one candidate is selected from the candidates presented as the identification information m421, the identification information specifying unit 112 displays information indicating the selected candidate and information indicating the region f42 associated with the identification information m421. Are obtained from the operation unit 204.
When it is difficult to directly acquire information indicating one candidate and information indicating the region f42 from the operation unit 204, the identification information specifying unit 112 determines the position in the image specified by the user U1. Information to be shown may be acquired from the operation unit 204. In this case, the identification information specifying unit 112 is selected by acquiring each area displayed in the image and the position information of the identification information from the display control unit 120 and collating with the position specified by the user U1. What is necessary is just to specify the information which shows one candidate, and the information which shows the area | region f42.
The identification information specifying unit 112 outputs information indicating one candidate selected by the user U1 and information indicating the region f42 to the extraction unit 114.
The extraction unit 114 acquires information indicating one candidate selected by the user U1 and information indicating the region f42 from the identification information specifying unit 112.
The extraction unit 114 extracts the co-occurrence probability data d14 corresponding to the detection target indicated by the acquired one candidate from the co-occurrence probability data storage unit 116. In the case of the example shown in FIG. 15, “miso soup” is selected from the candidate objects in the region f42 presented as the identification information m421. Therefore, the extraction unit 114 extracts the co-occurrence probability data d14b corresponding to “miso soup”.
Further, the extraction unit 114 specifies region data corresponding to the region f42 from the already acquired region data based on the information indicating the acquired region f42. When the region data corresponding to the region f42 is specified, the extraction unit 114 associates the acquired identification information indicating one candidate, that is, the identification information indicating “miso soup”, with the region data corresponding to the region f42.
The extraction unit 114 outputs the region data of the region f42 updated by associating the identification information, and the newly extracted co-occurrence probability data d14b to the estimation unit 118.
The estimation unit 118 acquires the region data corresponding to the updated region f42 and the co-occurrence probability data d14b from the extraction unit 114.
The estimation unit 118 updates the area data corresponding to the pre-update area f42 that has already been acquired with the area data corresponding to the newly acquired area f42 after update. Then, the estimation unit 118 confirms whether the identification information indicating the identification result of the object in the area indicated by the area data is associated with each acquired area data, and the identification information is associated. Unextracted area data is extracted again. In the example illustrated in FIG. 15, the estimation unit 118 extracts region data corresponding to the region f43.
For the region data corresponding to the extracted region f43, the estimation unit 118 multiplies the identification score d13c of the object in the region f43 by the acquired co-occurrence probability data d14, thereby obtaining a new identification score d13. 'Is calculated.
Here, in the example illustrated in FIG. 15, the estimation unit 118, as the co-occurrence probability data d14, the co-occurrence probability data d14a corresponding to the “fish dish” that is the object in the region f41, and the object in the region f42. Co-occurrence probability data d14a corresponding to “miso soup” is acquired.
Thus, when there are a plurality of co-occurrence probability data d14, the estimation unit 118 multiplies each of the co-occurrence probability data d14 by the identification score d13c, thereby obtaining the identification score d13 ′ as the co-occurrence probability data d14. Calculate for each. In the case of the example illustrated in FIG. 15, the estimation unit 118 determines the identification score d13 ′ obtained by multiplying the identification score d13c and the co-occurrence probability data d14a, and the identification score d13 obtained by multiplying the identification score d13c and the co-occurrence probability data d14b. 'And calculate.
Then, the estimation unit 118 sets the average of the identification scores d13 'calculated for each co-occurrence probability data d14 as the identification score d13' of the corresponding region.
Since the subsequent processing is the same as “processing based on image acquisition” and “processing based on region setting”, detailed description thereof is omitted. By appropriately repeating the above operations, an area indicating the object captured in the image is set in the image, and identification information indicating the object in the area is associated with the set area. Is displayed.
On the other hand, the image in which the area of the object captured in the image and the identification information indicating the object in the area are set as sample data for calculating the identification score d13 and the co-occurrence probability data d14. obtain.
As a specific example, “fish dish” and “miso soup” are detected as objects from the image acquired from the imaging unit 202. In this case, the partial image of the region corresponding to “fish dish” in the image can be sample data for calculating the identification score d13 indicating the accuracy of “fish dish”. In addition, “fish dishes” and “miso soup” are detected as objects from the image. Therefore, the image in which “fish dish” and “miso soup” are detected serves as sample data for calculating the co-occurrence probability between “fish dish” and “miso soup”.
Therefore, the information processing apparatus 10 according to the present embodiment may include an update unit 122 for updating the identification score d13 and the co-occurrence probability data d14 for each detection target.
Specifically, the update unit 122 acquires from the estimation unit 118 the acquired image, the region data corresponding to the region set in the image, and the identification information associated with the region data. Here, the identification information associated with the area data indicates the identification result of the object in the area indicated by the area data.
Specifically, the update unit 122 causes the feature amount data storage unit 110 to store the acquired image and region data associated with the identification information. For example, it is assumed that “fish dish” and “miso soup” are detected as objects from the acquired image. In this case, the partial image of the region corresponding to “fish dish” in the image can be sample data for calculating the identification score d13 indicating the accuracy of “fish dish”. Accordingly, the update unit 122 uses the image in which “fish dishes” and “miso soup” are detected as objects as feature data storage units as sample data for calculating the identification score d13 of “fish dishes” and “miso soup”. 110 is stored.
Further, the update unit 122 may store the acquired image and the area data associated with the identification information in the co-occurrence probability data storage unit 116. For example, an image in which “fish dish” and “miso soup” are detected serves as sample data for calculating the co-occurrence probability between “fish dish” and “miso soup”. Therefore, the update unit 122 uses, as sample data for calculating or updating the co-occurrence probability between “fish dishes” and “miso soup”, for images in which “fish dishes” and “miso soup” are detected as objects. And stored in the co-occurrence probability data storage unit 116.
As described above, by providing the update unit 122, the information processing apparatus 10 according to the present embodiment repeats the process of acquiring an image and recognizing and identifying an object in the image, It is possible to improve the accuracy of recognition and identification of the object from
In the above description, an example in which a candidate for identification information indicating an object in a region is selected from candidates presented as identification information has been described. However, user U1 directly selects identification information indicating an object in a region. You may enable it to input. In this case, the identification information specifying unit 112 may output the identification information indicating the object input by the user U1 and the information indicating the area to the extraction unit 114. The subsequent processing is the same as that in the case of selecting identification information candidates indicating the objects in the region.
Further, when an object in the area is identified by receiving the designation of the area, the object in the other area is selected based on the co-occurrence probability data d14 corresponding to the newly identified object. You may identify. For example, in the example illustrated in FIG. 14, the case where it is difficult to recognize the object in the region f43 as “rice” when the region f43 is designated is described. On the other hand, it is assumed that the object can be recognized as “rice” based on the identification score d13 of the object in the region f43. In this case, the information processing apparatus 10 extracts the co-occurrence probability data d14 corresponding to the identified “rice”. Then, the information processing apparatus 10 identifies the object in the region f42 from the “miso soup” and “soup” presented as candidates in the identification information m421 based on the extracted co-occurrence probability data d14 of “rice”. May be.
Moreover, although the example shown above demonstrated the example which recognizes and identifies the dish imaged in the image as a target object, a target object is not limited to a dish. As a specific example, by applying a face recognition technique, a person imaged in an image may be recognized and identified as a target part. For example, FIG. 16 is a diagram for explaining an application example of the information processing system 1 according to the present embodiment, and shows an example in the case of detecting a person imaged in an image by face recognition technology.
In the example shown in FIG. 16, a man and a woman are captured in the image p16, and a region f51 corresponding to the man and a region f52 corresponding to the woman are set based on the recognition result of the man and the woman's face. It is assumed that At this time, it is assumed that the user U1 sets the identification information m51 indicating “Taro Suzuki” as the identification information indicating the person in the region f51.
Reference is now made to FIG. FIG. 17 is a diagram for explaining an application example of the information processing system 1 according to the present embodiment. The estimation result of the person in the region f52 before and after the identification information m51 is set for the person in the region f51. Is shown. In FIG. 17, a list v161 shows the estimation result of the person in the area f52 before the identification information m51 is set for the person in the area f51. That is, the estimation result shown in the list v161 is the estimation result of the person in the region f52 based only on the identification score d13 of the person in the region f52.
On the other hand, the list v162 shows the estimation result of the person in the area f52 after the identification information m51 is set for the person in the area f51. That is, the list v162 is an estimation result of persons in the region f52 based on the identification score d13 of the person in the region f52 and the co-occurrence probability data d14 corresponding to “Taro Suzuki” indicated by the identification information m51. .
As shown in FIG. 13, for example, it is assumed that “Taro Suzuki” has few acquaintances of women in their 40s and has few opportunities to take pictures together. In this case, the co-occurrence probability between “Taro Suzuki” and a person corresponding to a woman in their 40s is low. Based on such a premise, for example, in the determination of only the person identification score d13 in the region f52, the person indicated by the reference symbol m522 is extracted as a candidate as shown in the list v161. On the other hand, by setting the identification information m51 for the person in the region f51, as shown in the list v162, the person indicated by the reference sign m522 is excluded from the candidates.
Further, when paying attention to women in their twenties, it is highly possible that the person in the region f52 is the person indicated by the reference symbol m521 in the determination of only the identification score d13 of the person in the region f52. On the other hand, by setting the identification information m51 for the person in the region f51, as shown in the list v162, the person in the region f52 has a reference code more than the person indicated by the reference code m521. The possibility of being a person (for example, a family member) indicated by m523 is increased.
Further, as indicated by the reference symbol m524 in the list v162, it is possible to estimate a person who has not been listed as a candidate with a low probability before setting the identification information m51 based on the co-occurrence probability with “Taro Suzuki”.
In addition, in the example in the case where the above-described object is used as a dish, the example of calculating the co-occurrence probability by accumulating sample data has been described. However, if the probability that one object and another object co-occur can be defined, the sample The method for accumulating data is not limited. For example, when the object is a dish, the co-occurrence probability may be defined based on the compatibility between dishes such as whether the food is good or bad. When a person is detected from an image, the co-occurrence probability may be defined based on a mail history between persons who are detection targets, a human relationship such as family or friends, and the like.
Further, the accuracy of the identification score d13 and the co-occurrence probability data d14 may be improved by sharing the sample data among a plurality of users. As a specific example, the feature amount data storage unit 110 and the co-occurrence probability data storage unit 116 may be managed on a cloud service such as a social network and shared among a plurality of users.
The configuration illustrated in FIG. 6 is merely an example, and the position of each configuration is not limited as long as the input / output relationship between the configurations of the information processing apparatus 10 and the user terminal 20 does not change. For example, it is good also as a stand-alone structure by including each structure of the information processing apparatus 10 in the user terminal 20. FIG. In the example illustrated in FIG. 6, the display control unit 120 is provided on the information processing apparatus 10 side. However, the display control unit 120 may be provided on the user terminal 20 side. In the configuration illustrated in FIG. 6, when attention is paid to the information processing apparatus 10, the estimation unit 118 corresponds to an example of an “identification information acquisition unit”. Similarly, when focusing on the user terminal 20, the configuration in which the display unit 206 acquires area data from the display control unit 120 acquires identification information indicating each candidate associated with the area data. This corresponds to an example of an “identification information acquisition unit”.
[1.3. processing]
Next, with regard to the flow of a series of processing of the information processing apparatus 10 according to the present embodiment, particularly characteristic “processing based on area setting”, “processing based on identification information setting”, and “display identification information” The processing will be described by paying attention to “the processing related to
First, a flow of a series of operations of the information processing apparatus 10 based on the setting of the area will be described with reference to FIG. 18 as an example where the user U1 changes the area set in the image. FIG. 18 is a flowchart showing the operation of the information processing apparatus 10 according to the present embodiment, and shows the operation when the region set in the image is changed.
For example, it is assumed that areas f41 and f42 are set in the image, and the object in the area f41 is identified as “rice”. At this time, when the user U1 changes the range of the region f42, the object designating unit 106 operates the information indicating the region f42 corrected by the operation of the user U1 and the region data of the corrected region f42. Acquired from the unit 204. The object specifying unit 106 outputs information indicating the region f42 and region data corresponding to the corrected region f42 to the accuracy specifying unit 108.
The accuracy specifying unit 108 acquires information indicating the region f42 and region data corresponding to the corrected region f42 from the object specifying unit 106. Based on the information indicating the acquired region f42, the accuracy specifying unit 108 specifies region data corresponding to the region f42 before correction from the already acquired region data. Then, the accuracy specifying unit 108 updates the area data corresponding to the specified area f42 before correction with the area data corresponding to the newly acquired area f42 after correction. Hereinafter, even when there is no particular description, the extraction unit 114, the estimation unit 118, and the display control unit 120 are notified of information indicating the region f42, so that the extraction unit 114, the estimation unit 118, and It is assumed that the display control unit 120 specifies area data corresponding to the area f42. However, the region data specifying method based on the information indicating the region f42 is merely an example, and the method is not limited as long as the region data corresponding to the region f42 can be specified.
When the region data corresponding to the region f42 is updated, the accuracy specifying unit 108 specifies the updated region f42 in the already acquired image, that is, the image displayed on the display unit 206. After specifying the region f42 in the image, the accuracy specifying unit 108 recalculates the identification score d13 of the object included in the region f42. Hereinafter, the identification score d13 of the object corresponding to the updated region f42 may be referred to as “identification score d13b”.
The accuracy specification unit 108 outputs the region data corresponding to the updated region f42 and the recalculated identification score d13b of the object in the region f42 to the extraction unit 114.
The extraction unit 114 acquires the region data corresponding to the updated region f42 and the recalculated identification score d13c of the object in the region f43 from the accuracy specifying unit 108.
The extraction unit 114 refers to the acquired identification score d13b and specifies a detection target in which the identification score d13b indicates a value equal to or greater than a predetermined threshold. The operation when the detection target can be specified is as described above in “Processing Based on Image Acquisition”. The extraction unit 114 extracts the co-occurrence probability data d14 corresponding to the detection target and the detection target for the region data. Is associated with identification information indicating.
The extraction unit 114 outputs the updated region data of the region f42 to the estimation unit 118. Further, when extracting the co-occurrence probability data d14 based on the identification score d13b of the object in the updated region f42, the extraction unit 114 outputs the extracted co-occurrence probability data d14 to the estimation unit 118.
Here, description will be made assuming that the extraction unit 114 cannot extract the co-occurrence probability data d14 based on the identification score d13b of the object in the updated region f42. Therefore, it is assumed that the area data corresponding to the updated area f42 is not associated with identification information indicating the object in the area f42.
The estimation unit 118 acquires the region data corresponding to the updated region f42 and the identification score d13b of the object in the region f42 from the extraction unit 114.
The estimation unit 118 confirms whether or not the identification information is associated with the area data corresponding to the acquired updated area f42. Here, it is assumed that identification information is not associated with the area data corresponding to the updated area f42.
The estimation unit 118 has already acquired the co-occurrence probability data d14 for the object in the region f41, that is, the co-occurrence probability corresponding to “rice” for the identification score d13b of the object in the updated region f42. By multiplying the data d14, a new identification score d13 ′ is calculated.
The estimation unit 118 determines the identification score d13 ′ corresponding to the object in the updated region f42 based on the criterion 1 and the criterion 2, and associates the identification information with the region data corresponding to the region f42 based on the determination result. .
The estimation unit 118 outputs region data corresponding to the updated region f42 to the display control unit 120.
The display control unit 120 acquires area data corresponding to the updated area f42 from the estimation unit 118. The display control unit 120 updates the area f42 already displayed in the image based on the acquired area data corresponding to the updated area f42.
In addition, the display control unit 120 extracts the identification information associated with the acquired updated area f42, and updates the identification information already displayed associated with the area f42 with the extracted identification information.
As described above, when the range of the region f42 is changed, the identification result of the object in the region f42 is updated based on the changed region f42, and is associated with the region f42 based on the updated identification result. The identified identification information is updated.
Next, a flow of a series of operations of the information processing apparatus 10 based on the setting of the identification information will be described with reference to FIG. 19, taking as an example the case where the user U1 sets the identification information for the area in the image. . FIG. 19 is a flowchart showing the operation of the information processing apparatus 10 according to this embodiment, and shows the operation when identification information is set for a region in an image.
For example, it is assumed that areas f41 and f42 are set in the image, and “miso soup” and “ramen” are presented as identification result candidates indicating the objects in the area f42. At this time, when the user U1 sets “rice” as the identification information indicating the object in the region f41, the identification information specifying unit 112 includes information indicating “rice” set as the identification information indicating the object; Information indicating the region f41 in which the “rice” is set is acquired from the operation unit 204.
The identification information specifying unit 112 outputs information indicating “rice” selected by the user U1 and information indicating the region f41 to the extraction unit 114.
The extraction unit 114 extracts the co-occurrence probability data d14a corresponding to “rice” from the co-occurrence probability data storage unit 116 based on the acquired information indicating “rice”.
Further, the extraction unit 114 specifies region data corresponding to the region f41 from the already acquired region data based on the information indicating the acquired region f41. When the region data corresponding to the region f41 is specified, the extraction unit 114 associates the identification information indicating “rice” with the region data corresponding to the region f41. In the following, even when there is no particular description, the estimation unit 118 and the display control unit 120 correspond to the region f41 by notifying the estimation unit 118 and the display control unit 120 of information indicating the region f41. The area data shall be specified.
The extraction unit 114 outputs the region data of the region f41 updated by associating the identification information and the co-occurrence probability data d14a corresponding to the newly extracted “rice” to the estimation unit 118.
The estimation unit 118 acquires the region data corresponding to the updated region f41 and the co-occurrence probability data d14a from the extraction unit 114.
The estimation unit 118 updates the area data corresponding to the area f41 before update that has already been acquired with the area data corresponding to the area f41 after update that has been newly acquired. Then, the estimation unit 118 confirms whether the identification information indicating the identification result of the object in the area indicated by the area data is associated with each acquired area data, and the identification information is associated. Unextracted area data is extracted again. The area f42 is associated with identification information indicating identification result candidates, not identification information indicating the identification result of the object. Therefore, here, description will be made on the assumption that the area data corresponding to the area f42 has been extracted.
For the region data corresponding to the extracted region f42, the estimation unit 118 multiplies the identification score d13 of the object in the region f42 by the acquired co-occurrence probability data d14, thereby obtaining the target in the region f42. The identification score d13 ′ corresponding to the object is recalculated.
Here, the estimation unit 118 acquires the co-occurrence probability data d14a corresponding to “rice” that is the object in the region f41, as the co-occurrence probability data d14. Therefore, the estimation unit 118 multiplies the identification score d13 of the object in the region f42 by the co-occurrence probability data d14a corresponding to “rice”, thereby identifying the identification score d13 corresponding to the object in the region f42. 'Will be recalculated.
The estimation unit 118 determines the identification score d13 ′ corresponding to the recalculated object in the region f42 based on the criterion 1 and the criterion 2, and adds identification information to the region data corresponding to the region f42 based on the determination result. Associate. Thereby, the area data corresponding to the area f42 is updated.
As described above, when the identification information indicating the object in the area f41 is set, the identification result of the object in the area f42 is updated based on the co-occurrence probability data corresponding to the identification result of the area f41. The identification information associated with the region f42 is updated based on the updated identification result.
(Processing related to the display of identification information)
Next, as processing related to the display of the identification information, the identification information indicating the identification result of the object in the region or the identification information indicating the candidate of the identification result is specified and the identified identification is performed with reference to FIG. An example of detailed processing for displaying information in association with a corresponding area will be described. FIG. 20 is a flowchart illustrating the operation of the information processing apparatus 10 according to the present embodiment. The identification information indicating the identification result of the object in the region or the identification information indicating the identification result candidate is identified and handled. The operation in the case of displaying in association with the area to be displayed is shown. In the following description, it is assumed that the identification score d13 ′ of the object in the region f41 has already been calculated by paying attention to the processing for one region f41 in the image.
When calculating the identification score d13 ′ of the object in the region f41, the estimation unit 118 generates a list of identification scores d13 ′ for each detection target, and sorts the list in descending order of the identification score d13 ′. Hereinafter, the list of identification scores d13 ′ may be referred to as “identification score list”.
(Steps S304 and S306)
The estimation unit 118 determines whether or not the identification score list is empty. If the identification score list is not empty (No in step S304), the estimation unit 118 extracts an identification score d13 ′ that is one detection target included in the identification score list. (Step S306).
The estimation unit 118 determines whether or not the identification score d13 ′ extracted from the identification score list satisfies the criterion 1.
When the extracted identification score d13 ′ satisfies the criterion 1 (step S308, Yes), that is, when the identification score d13 ′ is equal to or greater than the first threshold, the estimation unit 118 corresponds to the identification score d13 ′. The detection target to be recognized is recognized as the identification result of the target in the region f41. The first threshold is a threshold for the estimation unit 118 to identify the object based on the identification score d13 ′.
For example, when the identification score d13 ′ to be determined corresponds to “miso soup”, the estimation unit 118 identifies the object in the region f41 as “miso soup”. In this case, the estimation unit 118 corresponds to the region f41 using identification information indicating the detection target corresponding to the identification score d13 ′, that is, identification information indicating “miso soup” as identification information indicating the identification result of the target object. Associate with region data.
The estimation unit 118 outputs region data corresponding to the region f41 associated with the identification information to the display control unit 120. The display control unit 120 superimposes and displays the region f41 based on the acquired region data on the image displayed on the display unit 206. Then, the display control unit 120 displays the identification information indicating “miso soup” associated with the region data corresponding to the region f41 in association with the region f41 as the identification result of the object in the region f41.
In addition, when the identification score d13 ′ satisfying the criterion 1 is detected, the estimation unit 118 needs to make a determination based on the criterion 1 and the criterion 2 with respect to another identification score d13 ′ included in the identification score list. There is no.
When the extracted identification score d13 ′ does not satisfy the criterion 1 (No in step S308), that is, when the identification score d13 ′ is less than the first threshold, the estimation unit 118 determines that the identification score d13 ′ is It is determined whether or not criterion 2 is satisfied.
When the extracted identification score d13 ′ satisfies the criterion 2 (step S312, Yes), that is, when the identification score d13 ′ is equal to or greater than the second threshold, the estimation unit 118 corresponds to the identification score d13 ′. The detection target to be recognized is recognized as an identification result candidate indicating the target in the region f41. Note that the second threshold is a threshold for the estimation unit 118 to identify a candidate for an identification result indicating an object based on the identification score d13 ′.
The estimation unit 118 adds a detection target corresponding to the identification score d13 'satisfying the criterion 2 to a list indicating identification result candidates indicating the target in the region f41. Hereinafter, a list indicating identification result candidates indicating an object may be referred to as a “candidate list”.
When the extracted identification score d13 ′ does not satisfy the criterion 2 (No in step S312), that is, when the identification score d13 ′ is less than the second threshold, the estimation unit 118 determines the identification score d13 ′. The detection target corresponding to is not added to the candidate list.
As described above, the estimation unit 118 executes the processes according to steps S306 to S314 until the identification score d13 ′ satisfying the criterion 1 is detected or the identification score list becomes empty.
When the identification score list becomes empty (step S304, Yes), the estimation unit 118 checks whether or not the candidate list includes identification result candidates indicating the objects in the region f41.
When the candidate list is empty (step S316, Yes), the estimation unit 118 outputs the region data corresponding to the region f41 to the display control unit 120. The display control unit 120 superimposes and displays the region f41 based on the acquired region data on the image displayed on the display unit 206. At this time, the identification information is not associated with the area data corresponding to the area f41. Therefore, in this case, the display control unit 120 does not associate identification information with the region f41.
If the candidate list is not empty (step S316, No), the estimation unit 118 corresponds to the region f41 by using each piece of identification information included in the candidate list as a candidate of an identification result indicating the object in the region f41. Associate with region data. Then, the estimation unit 118 outputs region data corresponding to the region f41 associated with the identification information to the display control unit 120. The display control unit 120 superimposes and displays the region f41 based on the acquired region data on the image displayed on the display unit 206. Then, the display control unit 120 displays each piece of identification information associated with the region data corresponding to the region f41 in association with the region f41 as a candidate for an identification result indicating the object in the region f41.
As described above, the estimation unit 118 performs the determination based on the criterion 1 and the criterion 2 on the identification score d13 ′ of the object in the region f41, and the identification information indicating the identification result of the object in the region, or The identification information indicating the identification result candidate is specified. Thereby, the display control unit 120 displays identification information indicating the identification result of the object in the region f41 or identification information indicating the identification result candidate for the region f41 based on the determination result for the identification score d13 ′. It is possible to display them in association with each other.
[1.4. Example 1: Area designation method (pointer operation)]
Next, as Example 1, an example of a method for specifying an area in an image will be described. Here, an example of a method for designating an area in an image by operating the pointer c1 using a device such as a mouse or a trackball will be described.
First, referring to FIG. FIG. 21 is a diagram for explaining an example of a region specifying method according to the first embodiment, and illustrates an example in which a region having a predetermined shape and size is set at a specified position in an image. .
For example, a screen p171 in FIG. 21 shows a state before an area is set in the image. Reference numeral v17 is an area designation screen for designating an area to be set in the image (hereinafter referred to as “area designation section v17”). For example, in the example illustrated in FIG. 21, the area designating unit v17 includes an icon v171 for setting a rectangular area and an icon v172 for setting an elliptical area.
As shown on the screen p171, the user U1 clicks an icon corresponding to a desired area with the pointer c1 to select the type (namely, shape or size) of the area to be set. After selecting the type of area to be set, the user U1 clicks a desired position in the image as shown on the screen p172.
When a position in the image is clicked by the pointer c1 in response to an operation by the user U1, the information processing apparatus 10 sets an area of the selected type based on the position clicked by the pointer c1. In the example shown in FIG. 21, as shown on the screen p171, the user U1 clicks the icon v172 with the pointer c1. Therefore, the information processing apparatus 10 sets an elliptical region f61 in the image as shown on the screen p173.
(Delete area)
Next, an example of deleting an area set in an image will be described with reference to FIG. FIG. 22 is a diagram for explaining an example of a region designation method according to the first embodiment, and illustrates an example in which a region set in an image is deleted.
For example, a screen p174 in FIG. 22 shows a state where an area f61 is set in the image. As shown on the screen p174, the user U1 double-clicks part of the area f61 with the pointer c1.
When a part of the area f61 is double-clicked by the pointer c1 in response to an operation by the user U1, the information processing apparatus 10 displays an image of the area f61 designated by the double-clicking by the pointer c1 as shown in a screen p175. Remove from inside.
(Move area)
Next, an example of moving a region set in an image will be described with reference to FIG. FIG. 23 is a diagram for explaining an example of the area specifying method according to the first embodiment, and illustrates an example in which the area set in the image is moved.
For example, the screen p176 in FIG. 23 shows a state where an area f611 is set in the image. The user U1 drags a part of the area f611 with the pointer c1 as indicated by the reference number c176 on the screen p176.
When a part of the area f611 is dragged by the pointer c1 in response to the operation by the user U1, the information processing apparatus 10 causes the area f611 to follow the drag operation by the pointer c1, thereby indicating the area f612 on the screen p177. As described above, the position of the region f611 is changed. Note that a region f612 in the screen p177 indicates a region f611 after movement based on the drag operation.
(Resize area)
Next, an example of changing the size of a region set in an image will be described with reference to FIG. FIG. 24 is a diagram for explaining an example of the area designating method according to the first embodiment, and illustrates an example in which the size of the area set in the image is changed.
For example, a screen p178 in FIG. 24 shows a state where an area f621 is set in the image. The user U1 drags the outer edge f621a of the region f621 with the pointer c1, as indicated by the reference number c177 on the screen p178.
When the outer edge f621a of the area f621 is dragged by the pointer c1 in response to the operation by the user U1, the information processing apparatus 10 deforms the area f621 so that the outer edge f621a follows the drag operation by the pointer c1. As a result, the size of the region f621 is changed as shown in the region f622 of the screen p179. Note that an area f622 in the screen p179 indicates the area f621 whose size has been changed based on the drag operation.
(Rotate area)
Next, an example in which the orientation is changed by rotating an area set in the image will be described with reference to FIG. FIG. 25 is a diagram for explaining an example of the area specifying method according to the first embodiment, and illustrates an example in which the orientation is changed by rotating the area set in the image.
For example, the screen p180 in FIG. 25 shows a state where an area f631 is set in the image. When the user U1 clicks the area f631 in the screen p180 with the pointer c1, the information processing apparatus 10 displays the handle f631a. Next, as shown by the reference number c181 on the screen p180, the user U1 drags the handle f631a with the pointer c1.
When the handle f631a is dragged by the pointer c1 in response to an operation by the user U1, the information processing apparatus 10 rotates the region f631 so that the handle f631a follows the drag operation by the pointer c1. Thereby, the direction of the region f631 is changed as shown in the region f632 of the screen p181. Note that a region f632 in the screen p181 indicates a region f631 whose direction has been changed based on the drag operation.
Note that the above-described method of specifying an area in an image by operating the pointer c1 is merely an example. Therefore, it is not always necessary to realize all the designation methods shown above, and only a part of them may be adopted. The method is not limited to the above as long as an area can be designated in the image by operating the pointer c1.
[1.5. Example 2: Method for designating area (touch panel)]
Next, as Example 2, an example of a method for specifying an area in an image will be described. Here, an example of a method for designating an area in an image by an operation using a touch panel will be described.
(Area setting: Setting an area of existing shape and size)
First, referring to FIG. FIG. 26 is a diagram for explaining an example of the area designating method according to the second embodiment, and illustrates an example in which an area having a predetermined shape and size is set at a designated position in the image. .
For example, a screen p211 in FIG. 26 shows a state before an area is set in the image. As shown in the screen p211, the user U1 taps a desired position in the image with a finger, a stylus, or the like (hereinafter may be referred to as “finger c2”).
When the position in the image is tapped by the user U1, the information processing apparatus 10 sets an area having a predetermined shape and size with reference to the tapped position. In the example illustrated in FIG. 26, the information processing apparatus 10 sets a rectangular region f64 in the image as illustrated on the screen p212.
(Area setting: Setting an area of any size)
Next, an example of setting an area of an arbitrary size in an image will be described with reference to FIG. FIG. 27 is a diagram for explaining an example of a region designation method according to the second embodiment, and illustrates an example in which a region of an arbitrary size is set in an image.
For example, a screen p213 in FIG. 26 shows a state before an area is set in the image. As indicated by the reference number c213 on the screen p213, the user U1 drags with a finger c2 starting from a part of the image where no region is set.
When the user U1 performs a drag operation starting from a part where an area in the image is not set, the information processing apparatus 10 is a rectangle having a diagonal line from the start point to the end point of the drag operation as shown in a screen p214. Is set in the image.
(Area setting: Setting an area of any shape and size)
Next, an example of setting a region having an arbitrary shape and size in an image will be described with reference to FIG. FIG. 28 is a diagram for explaining an example of a region designation method according to the second embodiment, and illustrates an example in which a region having an arbitrary shape and size is set in an image.
For example, a screen p215 in FIG. 28 shows a state before an area is set in the image. The user U1 drags with the finger c2 so as to surround a desired area in the image as indicated by a reference number c215 on the screen p215.
When the user U1 performs a drag operation so as to surround an area in the image, the information processing apparatus 10 sets the area surrounded by the drag operation with the finger c2 as an area f66 in the image, as shown in a screen p216. Set.
Next, another example of setting an area having an arbitrary shape and size in an image will be described with reference to FIG. FIG. 29 is a diagram for explaining an example of a region specifying method according to the second embodiment, and illustrates another example in which a region having an arbitrary shape and size is set in an image.
For example, a screen p217 in FIG. 29 shows a state before an area is set in the image. As indicated by the reference number c217 on the screen p217, the user U1 drags with the finger c2 so as to fill a desired area in the image.
When the user U1 performs a drag operation so as to fill an area in the image, the information processing apparatus 10 sets the area painted by the drag operation with the finger c2 as an area f66 in the image as shown in a screen p216. Set.
(Area setting: Automatic setting based on image processing)
Next, an example in which the information processing apparatus 10 automatically sets a region based on image processing in response to designation by the user U1 will be described with reference to FIG. FIG. 30 is a diagram for explaining an example of a region designation method according to the second embodiment, and illustrates an example in which the information processing apparatus 10 automatically sets a region based on image processing in response to designation by the user U1. Yes.
For example, a screen p219 in FIG. 30 shows a state before an area is set in the image. As shown on screen p219, user U1 taps a desired position in the image with finger c2.
When the position in the image is tapped by the user U1, the information processing apparatus 10 uses the tapped position as a reference position, and based on image processing, a region that is continuous from the reference position and similar in color to the reference position. Identify. Then, the information processing apparatus 10 sets the specified area in the image as the area f67 as shown in the screen p220.
Next, an example of moving an area set in an image will be described with reference to FIG. FIG. 31 is a diagram for explaining an example of the area specifying method according to the second embodiment, and illustrates an example in which an area set in an image is moved.
For example, a screen p221 in FIG. 31 shows a state where an area f681 is set in the image. The user U1 drags part of the area f681 with the finger c2 as indicated by the reference number c221 on the screen p221.
When a part of the region f681 is dragged by the user U1, the information processing apparatus 10 causes the region f681 to follow the drag operation with the finger c2, thereby changing the position of the region f681 as shown in the region f682 on the screen p222. change. Note that an area f682 in the screen p222 indicates the area f681 after the movement based on the drag operation.
Next, an example of changing the size of a region set in an image will be described with reference to FIG. FIG. 32 is a diagram for explaining an example of the area designating method according to the second embodiment and illustrates an example in which the size of the area set in the image is changed.
For example, a screen p223 in FIG. 32 shows a state where an area f691 is set in the image. As shown by reference numbers c223a and c223b on the screen p223, the user U1 performs a pinch operation with a finger c2a and c2b on part of the area f691. In the example illustrated in FIG. 32, the user U1 performs a pinch-in operation on the region f691.
When the user U1 performs a pinch operation on part of the area f691, the information processing apparatus 10 deforms the area f691 so as to follow the pinch operation with the fingers c2a and c2b. Thereby, the size of the area f691 is changed as shown in the area f692 of the screen p224. Note that a region f692 in the screen p224 indicates a region f691 whose size has been changed based on a pinch operation. Needless to say, the area size is reduced by the pinch-in operation, and the area size is increased by the pinch-out operation.
Next, an example of deleting an area set in an image will be described with reference to FIG. FIG. 33 is a diagram for explaining an example of the area designating method according to the second embodiment, and illustrates an example in which an area set in an image is deleted.
For example, a screen p225 in FIG. 33 shows a state where an area f70 is set in the image. As shown on the screen p225, the user U1 double taps part of the area f70 with the finger c2.
When part of the area f70 is double-tapped by the user U1, the information processing apparatus 10 deletes the area f70 designated by double-tapping with the finger c2 from the image as shown on the screen p226.
Next, another example of deleting a region set in an image will be described with reference to FIG. FIG. 34 is a diagram for explaining an example of a method for specifying an area according to the second embodiment, and illustrates another example of deleting an area set in an image.
For example, a screen p227 in FIG. 34 shows a state where an area f70 is set in the image. When the user U1 taps the area f70 in the screen p227 with the finger c2, the information processing apparatus 10 displays the delete icon f70a. On the screen p227, the information processing apparatus 10 displays the delete icon f70a on the upper right end of the region f70. Next, the user U1 taps the delete icon f70a with the finger c2.
When the delete icon f70a is tapped by the user U1, the information processing apparatus 10 deletes the area f70 corresponding to the tapped delete icon f70a from the image as shown on the screen p228.
(Division of area)
Next, an example of dividing an area set in an image into a plurality of areas will be described with reference to FIG. FIG. 35 is a diagram for explaining an example of a region designation method according to the second embodiment, and illustrates an example in which a region set in an image is divided into a plurality of regions.
For example, a screen p231 in FIG. 35 shows a state where an area f711 is set in the image. As indicated by reference number c231 on the screen p231, the user U1 performs a drag operation with the finger c2 so as to divide the region f711.
When the user U1 performs a drag operation so as to divide the region f711, the information processing apparatus 10 uses the drag operation locus indicated by the reference number c231 as a boundary to display the region f711 as illustrated in the screen p232. The area is divided into a plurality of areas f712 and f713.
(How to set the area by manipulating the image)
In the above description, an example of setting or operating an area in an image has been described. Here, a method of setting an area in an image by operating the image side will be described with reference to FIGS. When setting an area in an image by operating the image side, for example, a dedicated editing mode is prepared in advance, and setting is performed by starting the editing mode. The operation can be differentiated from the normal screen operation. Hereinafter, an example will be described in which an area in an image is set by an operation on the image side by activating the edit mode.
First, refer to FIG. FIG. 36 is a diagram for explaining an example of the area designating method according to the second embodiment, and illustrates an example when the edit mode is activated.
For example, a screen p311 in FIG. 36 shows a screen before starting the edit mode, that is, a screen for performing a normal operation. Reference sign v311 indicates an edit start button for starting the edit mode. The user U1 taps the edit start button v311 as shown on the screen p311.
When the edit start button v311 is tapped by the user U1, the information processing apparatus 10 activates the edit mode. In the edit mode, the information processing apparatus 10 displays a U / I indicating that an area to be set in the image can be identified, as shown on a screen p312. In the example shown on the screen p312, the information processing apparatus 10 superimposes and displays a shaded v313 with a part of the opening provided on the image. Note that an area is set in the partial image displayed in the opening portion of the shaded v313 in the image.
When the information processing apparatus 10 starts the edit mode, the information processing apparatus 10 displays an edit end button v312 instead of the edit start button v311. When the edit end button v312 is tapped by the user U1, the edit mode ends.
Next, an operation for setting an area will be described with reference to FIG. FIG. 37 is a diagram for explaining an example of the area designating method according to the second embodiment, and illustrates an example of designating an area by operating the image side.
First, the screen p313 is referred to. As indicated by reference numeral c313, the user U1 drags the image with the finger c2, and adjusts the position of the image so that the portion in the image where the region is desired to be set is included in the opening portion of the shaded v313. . In the example shown on the screen p313, the user U1 moves the screen so that the lower right area of the image is included in the opening portion of the shaded v313 by dragging the screen to the upper left as shown by the reference number c313. ing.
Next, the screen p314 is referred to. The user U1 changes the display size of the image by performing a pinch operation with the fingers c2a and c2b as indicated by reference numbers c314a and c314b. For example, in the example shown on the screen p314, the user U1 increases the display size of the image by performing a pinch-out operation as indicated by reference numbers c314a and c314b. As a result, the size of the region in the image surrounded by the opening portion of the shaded v313 is relatively smaller than before the operation. In addition, when pinch-in operation is performed, it cannot be overemphasized that the size of the area | region in an image enclosed from the opening part position of shaded v313 becomes relatively large compared with before operation.
Here, the screen p315 is referred to. A screen p315 shows a state after the display size of the image is changed in accordance with the pinch operation. As described above, it is possible to specify the size of an area in an image by changing the display size of the image by a pinch operation.
Next, an operation for ending the editing mode will be described with reference to FIG. FIG. 38 is a diagram for explaining an example of the area designating method according to the second embodiment, and shows an example when the edit mode is ended.
First, the screen p316 is referred. A screen p316 shows a state in which the designation of the area is completed based on the operation on the image side, and corresponds to the screen p315 in FIG. The user U1 taps the edit end button v312 as shown on the screen p316.
When the edit end button v312 is tapped by the user U1, the information processing apparatus 10 sets an area in the image included in the opening of the shaded v313 as an area f72 as shown on the screen p317. After setting the area f72, the information processing apparatus 10 ends the editing mode. In addition, when the information processing apparatus 10 ends the editing mode, the information processing apparatus 10 displays an editing start button v311 instead of the editing end button v312.
Note that the method for specifying an area in an image by operating the touch panel as described above is merely an example. Therefore, it is not always necessary to realize all the designation methods shown above, and only a part of them may be adopted. Further, the method is not limited to the above method as long as an area can be designated in the image by operating the touch panel.
[1.6. Example 3: Display method of identification information]
Next, as Example 3, an example of a method for displaying identification information indicating a candidate for an object captured in an image will be described. In the above-described embodiment, for example, as shown in the identification information m421 in FIG. On the other hand, among the presented candidates, there may be cases where the identification score d13 ′ indicating the probability that the target object is the candidate is high and low. Therefore, in the third embodiment, the information processing apparatus 10 presents the difference in the probability that the target is the candidate according to the identification score d13 ′ corresponding to each candidate of the target identification result so as to be identifiable. An example will be described.
First, FIG. 39 will be referred to. FIG. 39 is a diagram for explaining an example of the display mode according to the third embodiment, and illustrates an example of information for presenting a difference in probability that the target object is the candidate. Data d21 shown in FIG. 39 indicates an icon d211 for presenting a difference in the identification score d13 ', and as indicated by a reference number d212, an identification score d13' in a different range is associated with each icon d211. For example, in the data d21, the case where the identification score d13 'is “0.75 or more” is associated with the icon v51a. Similarly, when the identification score d13 'is "0.5 or more and less than 0.75", it is associated with the icon v51b. Further, when the identification score d13 'is "0.25 or more and less than 0.5", it is associated with the icon v51c, and when the identification score d13' is "less than 0.25", it is associated with the icon v51d.
Reference is now made to FIG. FIG. 40 illustrates an example of a display mode according to the third embodiment. For example, in the screen p321 in FIG. 40, a region f81 corresponding to “white rice” is set in the image, and “white rice” and “white rice” are displayed in the region f81 as candidates for identification results indicating objects in the region f81. Identification information m811 in which “brown rice” is presented is set. It should be noted that an icon v51a indicating that the identification score d13 'is 0.75 or more is associated with "white rice rice" in the identification information m811 and presented. Similarly, “brown rice” in the identification information m811 is presented in association with an icon v51d indicating that the identification score d13 ′ is less than 0.25.
In this case, the display control unit 120 of the information processing apparatus 10 estimates the identification score d13 ′ calculated for each candidate in addition to the area data corresponding to the area f81 associated with the identification information indicating each candidate. What is necessary is just to acquire from the part 118. Then, the display control unit 120 determines which range indicated by the data d21 the identification score d13 ′ for each candidate corresponds to, and based on the determination result, displays an icon associated with the candidate with the icons v51a to v51d. You can specify from. The data d21 may be stored in a place where the display control unit 120 can read it.
Here, attention is again paid to the screen p321 in FIG. On the screen p321, a region f821 corresponding to a part of “miso soup” is set in the image, and “miso soup” and “ramen” are presented as candidates of identification results indicating the object in the region f821 in the region f821. The identification information m821 is set. It should be noted that an icon v51c indicating that the identification score d13 'is not less than 0.25 and less than 0.5 is presented in association with "miso soup" in the identification information m821. Similarly, an icon v51d indicating that the identification score d13 'is less than 0.25 is presented in association with "ramen" in the identification information m821.
Next, attention is focused on the screen p322 in FIG. The screen p322 shows a state in which the range of the region f821 is edited by the user U1 so as to include all the regions corresponding to “miso soup” in the image with reference to the state of the screen p321. Note that a region f822 shows the region f821 after the range has been changed.
As shown in the region f822 in the screen p322, the region corresponding to the “miso soup” in the image is updated, so that the accuracy identification unit 108 updates the object identification score d13 in the region f822. Upon receiving the update result of the identification score d13, the estimation unit 118 recalculates the identification score d13 '. Then, the display control unit 120 acquires the recalculation result of the identification score d13 'by the estimation unit 118, and updates the icon associated with each candidate indicated in the identification information m821. The identification information m822 on the screen p322 shows the identification information m821 updated based on the recalculation result of the identification score d13 '.
As shown in the identification information m822, the icon associated with each candidate of the identification result indicating the object in the region f822 is updated. Specifically, the icon associated with “miso soup” in the identification information m822 is updated from the icon v51c to the icon v51b, and the icon corresponding to “ramen” is updated from the icon v51d to the icon v51c.
With such a configuration, the user U1 updates the area indicating “miso soup” in the image from the area f821 to the area f822, whereby the accuracy corresponding to each candidate of identification information indicating the object in the area f822 is obtained. Can be recognized.
Next, refer to FIG. FIG. 41 illustrates an example of a display mode according to the third embodiment. A screen p322 in FIG. 41 corresponds to the screen p222 in FIG.
Next, attention is focused on the screen p323 in FIG. Screen p323 shows a state in which “white rice” is selected from the candidates shown in identification information m811 by user U1, with reference to the state of screen p322. The identification information m812 indicates the identification information m811 that has been updated by selecting “white rice rice” from the candidates indicated in the identification information m811.
When “white rice” is selected from the candidates shown in the identification information m811, the extraction unit 114 extracts the co-occurrence probability data d14 corresponding to “white rice” from the co-occurrence probability data storage unit 116. To the estimation unit 118. Based on the co-occurrence probability data d <b> 14 of “white rice” acquired from the extraction unit 114, the estimation unit 118 recalculates the identification score d <b> 13 ′ corresponding to the object in the region f <b> 822. Then, the display control unit 120 acquires the recalculation result of the identification score d13 'by the estimation unit 118, and updates the icon associated with each candidate indicated in the identification information m822. The identification information m823 on the screen p323 indicates the identification information m822 updated based on the recalculation result of the identification score d13 '.
As shown in the identification information m823, the icon associated with each candidate of the identification result indicating the object in the region f823 is updated. Specifically, the icon associated with “miso soup” in the identification information m823 is updated from the icon v51b to the icon v51c, and the icon corresponding to “ramen” is updated from the icon v51c to the icon v51d. With such a configuration, the user U1 identifies that the object in the region f81 is identified as “white rice rice”, thereby increasing the probability that the object in the region f822 is “miso soup”. It can be recognized by the change of the icon in m823.
[1.7. Summary]
As described above, the information processing system 1 according to the present embodiment receives the recognition and identification result of one object in the image and determines the co-occurrence probability between the one object and the other object. Based on this, other objects are identified. With such a configuration, when the user U1 sets identification information for one object in the image, the information processing system 1 is configured so that the specified one object and another object are between Based on the co-occurrence probability, an identification result of another object is estimated and presented to the user U1. Therefore, the user U1 can omit or simplify operations related to setting identification information for some objects in the image, and a series of operations related to setting identification information is simplified.
[2.1. Overview]
Next, an information processing system 1 ′ according to the second embodiment will be described. As described in the first embodiment, the accuracy of the identification score d13 and the co-occurrence probability data d14 is not limited by the number of sample data for calculating each value. Therefore, in the information processing system 1 ′ according to the second embodiment, the estimation result of the identification information indicating the object in the screen is presented to the user U1, and whether the estimation result is correct is determined and acquired. Sample data based on the determination result is accumulated. As described above, the information processing system 1 ′ according to the second embodiment re-learns by accumulating the sample data based on the determination result of the user U1 with respect to the estimation result of the object, and the identification score d13 and the co-occurrence probability data The accuracy of d14, that is, the accuracy related to the identification of the object is improved. Hereinafter, an example of the information processing system 1 ′ according to the present embodiment will be described.
First, an overview of the information processing system 1 'according to the present embodiment will be described with reference to FIG. FIG. 42 is a diagram for describing an overview of an information processing system 1 ′ according to the second embodiment of the present disclosure.
In the example shown in FIG. 42, “bread”, “coffee”, and “sausage” are captured as objects in the image p41, and regions f91 to f93 are set in the image p41 based on the recognition results of these objects. Has been.
In the information processing system 1 ′ according to the present embodiment, the user U1 determines whether or not the estimation result is correct with respect to the estimation result of the object in the region presented by the information processing system 1 ′. Then, identification information indicating the object is set. Therefore, the information processing system 1 ′ presents the result presentation unit v <b> 61 that is a U / I for presenting the estimation results of the objects included in the regions f <b> 91 to f <b> 93 together with the image p <b> 41.
The information processing system 1 'presents the estimation result of the object in the image p41 to the result presentation unit v61. As a specific example, in the example shown in FIG. 42, the result presentation unit v61 presents result presentation units v611 to v613 as regions for presenting the estimation results of the objects in the regions f91 to v93. The result presentation unit v611 presents the estimation result of the object in the region f91. Similarly, the result presentation unit v612 presents the estimation result of the object in the region f92, and the result presentation unit v613 presents the estimation result of the object in the region f93. In addition, when there is no particular distinction between the result presentation units v611 to v613, the result presentation unit v611 may be described as “result presentation unit v610”.
Here, attention is focused on the result presentation unit v611. The result presentation unit v611 includes an icon v62a, an estimation result v63, a positive button v64a, and a negative button v64b.
The icon v62a is an icon that presents the high level of proficiency of the information processing system 1 'with respect to the estimation of the object in the corresponding region f91, and shows a case where the proficiency is high. On the other hand, the icon v62b shown in the result presentation unit v613 is an icon presented so that the level of proficiency can be identified in the same manner as the icon v62a, and shows a case where the proficiency level is low. Hereinafter, when the icons v62a and v63b are not particularly distinguished, they may be simply referred to as “icon v62”. The identification result v63 displays the estimation result of the object in the area f91 by the information processing system 1 '.
Here, the “skill level” indicates the height of the experience value regarding the estimation (that is, identification) of the “object” presented by the information processing system 1 ′ as the estimation result v63. For example, the estimation result v63 The amount of sample data in which the “object” presented as is estimated is shown.
For example, when the information processing system 1 ′ identifies “bread” shown in the region f <b> 91, an image obtained by capturing “bread” and a result of identifying “bread” in the image increases. The information processing system 1 ′ can more accurately identify “bread” from the image. At this time, an image obtained by capturing “bread” and a result of identifying “bread” in the image serve as sample data for identifying “bread” from the image.
For this reason, the information processing system 1 ′ presents, as an icon v <b> 62, the proficiency level for identifying the object presented as the estimation result v <b> 63 so that the reliability of the estimation result v <b> 63 can be identified.
The affirmative button v64a and the negative button v64b are buttons for the user U1 to input a determination result as to whether the estimation result v63 presented by the information processing system 1 'is correct. The affirmative button v64a is a button for notifying the information processing system 1 'that the estimation result v63 is correct. The negative button v64b is a button for notifying the information processing system 1 'that the estimation result v63 is incorrect.
When the affirmative button v64a is operated by the user U1, the information processing system 1 ′ identifies the object in the corresponding area as the object indicated by the estimation result v63, and the estimation result v63 for the area. The identification information indicating the indicated object is displayed in association with each other. For example, in the case of the example shown in the result presentation unit v611, the information processing system 1 ′ receives an operation on the affirmative button v64a and associates identification information indicating “bread” presented in the estimation result v63 with the region f91. To display.
When the affirmative button v64a is operated, the information processing system 1 'stores the identification result of the target object in the corresponding area as sample data for estimating the target object. As a specific example, in the example illustrated in FIG. 42, the information processing system 1 ′ includes an image p41, region data corresponding to the region f91, and information indicating that the object in the region f91 is “bread”. Are stored as sample data. Thereby, for example, the information processing system 1 ′ can calculate the identification score d <b> 13 for identifying the object in the image as “bread” in consideration of newly added sample data.
In addition, when the user U1 is instructed that the estimation result is correct based on the operation of the affirmative button v64a with respect to the estimation result v63 corresponding to the plurality of objects in the image, between the objects It can be sample data for calculating the co-occurrence probability data d14.
As a specific example, in FIG. 42, it is assumed that the affirmative button v64a is operated in both the result presentation units v611 and v612. In this case, the object in the region f91 set in the image p41 is identified as “bread”, and the object in the region f92 is identified as “coffee”. In this case, the information processing system 1 'stores the identification results of the objects in the regions f91 and f92 as sample data for calculating the co-occurrence probability between the both identified objects. For example, in the case of the example in FIG. 42, the information processing system 1 ′ has the image p41, the region data corresponding to the regions f91 and f92, and the object in the region f91 is “bread”, Information indicating that the object is “coffee” is associated and stored as sample data. Thereby, for example, the information processing system 1 ′ can calculate the co-occurrence probability between “bread” and “coffee” in consideration of newly added sample data.
On the other hand, when the negative button v64b is operated, the information processing system 1 ′ displays the identification result of the target object in the corresponding area as the target object indicated by the estimation result v63 in the target area. It is stored as sample data indicating a difference.
For example, attention is paid to the region f93 and the result presentation unit v613. “Sausage” is imaged in the area f93, but the information processing system 1 ′ has a low level of proficiency for identifying “sausage”, and the object in the area f93 is “Yakiniku”. The indicated identification result v63 is presented. Here, it is assumed that the user U1 operates the negative button v64b for the result presentation unit v613.
In this case, the information processing system 1 ′ associates the image p41, the region data corresponding to the region f93, and information indicating that the object in the region f93 is not “barbecue” and stores it as sample data. To do. Thereby, for example, when the information processing system 1 ′ acquires another image similar to the partial image in the region f93, the object in the other image is added to the newly added sample data. Thus, the probability of estimating “Yakiniku” is low. In other words, by accumulating sample data based on the operation of the negative button v64b, when the information processing system 1 ′ acquires a similar image, it indicates that the object in the image is “roast meat”. The identification score d13 can be presented as a negative value.
Note that an icon imitating a character may be adopted as the icon v62 indicating the proficiency level, and the display mode of the icon v62 may be controlled so that the character grows according to the proficiency level of the information processing system 1 '. For example, FIG. 43 is a diagram showing an example of a display mode that changes with the proficiency level.
In the example shown in FIG. 43, the information processing system 1 ′ displays an icon when the proficiency level of the information processing system 1 ′ is low, such as when the user U1 has just started using the information processing system 1 ′. v62c is presented. Then, the information processing system 1 ′ may change the display mode of the icon v <b> 62 as indicated by the icon v <b> 62 d in accordance with the improvement in the proficiency level associated with the use of the information processing system 1 ′ by the user U <b> 1.
Note that the growth of the character indicated by the icon v62 as shown in FIG. 43 may be controlled based on, for example, the total number of sample data for each detection target. In this case, for example, the information processing system 1 ′ may present the total number of sample data as the character growth degree and present the proficiency level for each detection target as a change in the facial expression of the character. As a result, the information processing system 1 ′ can separately present the proficiency level based on the total number of sample data and the proficiency level based on the number of sample data for each detection target.
As described above, by controlling so that the icon v62 grows according to the proficiency level of the information processing system 1 ′, it is possible to give a game property to an operation related to setting identification information for an object in an image. It becomes. Accordingly, for example, in a system that performs calorie management based on the identified dish by setting identification information for the dish imaged in the image, the user U1 is motivated to set the identification information. An encouraging effect can be expected.
[2.2. processing]
Next, a series of operations of the information processing system 1 ′ according to the present embodiment will be described with reference to FIG. FIG. 44 is a flowchart showing the operation of the information processing apparatus according to the present embodiment.
The object extraction unit 104 acquires an image from the image acquisition unit 102. The target object extraction unit 104 extracts a region formed with a specific shape included in the acquired image or a region where a characteristic color component or texture exists as a target region. The object extraction unit 104 generates area data indicating the extracted area for each extracted area. The object extraction unit 104 outputs the acquired image and region data corresponding to each region extracted from the image to the accuracy specifying unit 108.
After specifying the region in the image, the accuracy specifying unit 108 identifies the object included in the specified region. Specifically, the accuracy specifying unit 108 compares the feature amount determined from each region in the image with the feature amount stored in advance for each detection target, thereby identifying the identification score of the target object included in the region. d13 is calculated for each detection target.
When the detection target whose identification score d13 is equal to or greater than the threshold value can be specified, the extraction unit 114 extracts the co-occurrence probability data d14 corresponding to the specified detection target from the co-occurrence probability data storage unit 116.
The extracting unit 114 estimates the acquired image, the region data corresponding to the region extracted from the image, the identification score d13 for each object in the region, and the extracted co-occurrence probability data d14. Output to.
For each acquired region data, the estimation unit 118 multiplies the identification score d13 of the target object by the co-occurrence probability data d14 corresponding to the target object in the region indicated by the other region data, thereby performing new identification. Score d13 ′ is calculated. After calculating the identification score d13 ', the estimation unit 118 estimates the object in the area indicated by the area data based on the calculated identification score d13'. At this time, the estimation unit 118 may use the object having the highest identification score d13 'as the object estimation result. When there is no co-occurrence probability data d14 corresponding to the object in the area indicated by the other area data, the estimation unit 118 sets the identification score d13 corresponding to the object in the area indicated by the area data. The identification score d13 ′ may be used.
When the target is estimated, the estimation unit 118 calculates a proficiency level related to the identification of the estimated target. As a specific example, the estimation unit 118 may calculate the number of sample data for identifying the estimated object, and determine the proficiency level based on the calculated number of sample data.
The estimation unit 118 associates identification information indicating the estimation result of the object with information indicating the calculated proficiency level for each acquired region data. The estimation unit 118 outputs the acquired image and region data corresponding to the region extracted from the image to the display control unit 120.
In addition, the display control unit 120 superimposes an area based on the acquired area data on the image displayed on the display unit 206.
Next, the display control unit 120 extracts identification information and a proficiency level associated with each area data. The display control unit 120 generates a result presentation unit v610 for each region data based on the extracted identification information and proficiency level, and associates the result presentation unit v610 with the corresponding region data. Then, the display control unit 120 generates a result presentation unit v61 that presents each result presentation unit v610 generated for each region data, and causes the display unit 206 to display the result presentation unit v61.
Here, it is assumed that the user U1 has operated the affirmative button v64a or the negative button v64b presented to each result presentation unit v610 for each region data via the operation unit 204. In this case, the identification information specifying unit 112, for each region data, information indicating the region data, identification information of the object presented as the estimation result v63 in the result presenting unit v610, an affirmative button v64a or a negative button The determination result based on v64b is acquired. The identification information specifying unit 112 outputs the information indicating the region data, the identification information of the target object, and the determination result acquired for each region data to the extraction unit 114.
The extraction unit 114 acquires information indicating the region data, identification information of the object, and the determination result from the identification information designating unit 112 for each region data. The extraction unit 114 refers to the determination result and the identification information of the object for each region data, and when the determination result based on the negative button v64b exists, the identification information associated with the determination result based on the positive button v64a Co-occurrence probability data d14 corresponding to may be newly acquired. In this case, the estimation unit 118 obtains the newly acquired co-occurrence probability data d14 from the extraction unit 114, thereby re-identifying the object whose estimation result is denied based on the negative button v64b. It is possible to calculate d13 ′ and present a new estimation result.
Further, the update unit 122 acquires, from the estimation unit 118, for example, an image in which the region indicated by each region data is set and each region data. In addition, the update unit 122 acquires the information indicating the region data, the identification information of the target object, and the determination result output from the identification information specifying unit 112 for each region data. At this time, the update unit 122 may directly acquire the information indicating the region data, the identification information of the target object, and the determination result from the identification information specifying unit 112, or via the extraction unit 114 and the estimation unit 118. You may get it.
The update unit 122 identifies the corresponding region data based on the information indicated by the acquired region data, and associates the identified region data with the identification information and determination result of the object associated with the information indicated by the region data.
The update unit 122 associates the acquired image, the region data corresponding to the region set in the image, the identification information of the target object, and the determination result, and the feature amount data storage unit 110 and the co-occurrence as sample data The data is stored in the probability data storage unit 116.
As described above, the identification score d13 and the co-occurrence probability data d14 are calculated in consideration of the newly added sample data. That is, the sample data for calculating the identification score d13 and the co-occurrence probability data d14 is updated, and the accuracy of the identification score d13 and the co-occurrence probability data d14 is improved.
[2.3. Summary]
As described above, in the information processing system 1 ′ according to the present embodiment, the estimation result of the identification information indicating the object in the screen is presented to the user U1, and whether the estimation result is correct is determined as the determination result. The sample data based on the determined result is accumulated. As described above, the information processing system 1 ′ according to the second embodiment re-learns by accumulating the sample data based on the determination result of the user U1 with respect to the estimation result of the object, and the identification score d13 and the co-occurrence probability data It is possible to improve the accuracy of d14, that is, the accuracy relating to the identification of the object.
The embodiment of the present disclosure has been described above. Information processing by the information processing apparatus 10 described above is realized in cooperation with the hardware of the information processing apparatus 10 as described below.
FIG. 45 is an explanatory diagram illustrating an example of a hardware configuration of the information processing apparatus 10. As illustrated in FIG. 12, the information processing apparatus 10 includes a CPU (Central Processing Unit) 901, a ROM (Read Only Memory) 902, a RAM (Random Access Memory) 903, an input device 908, an output device 910, and the like. A storage device 911, a drive 912, and a communication device 915.
The CPU 901 functions as an arithmetic processing device and a control device, and controls the overall operation in the information processing device 10 according to various programs. Further, the CPU 901 may be a microprocessor. The ROM 902 stores programs used by the CPU 901, calculation parameters, and the like. The RAM 903 temporarily stores programs used in the execution of the CPU 901, parameters that change as appropriate during the execution, and the like. These are connected to each other by a host bus including a CPU bus.
The input device 908 includes input means such as buttons and switches for the user to input information, an input control circuit that generates an input signal based on the input by the user, and outputs the input signal to the CPU 901. A user of the information processing apparatus 10 can instruct the information processing apparatus 10 to perform a processing operation by operating the input device 908.
The output device 910 includes a display device such as a liquid crystal display (LCD) device, an OLED (Organic Light Emitting Diode) device, and a lamp. Furthermore, the output device 910 includes an audio output device such as a speaker and headphones. For example, the display device displays a captured image or a generated image. On the other hand, the audio output device converts audio data or the like into audio and outputs it.
The storage apparatus 911 is a data storage apparatus configured as an example of a storage unit of the information processing apparatus 10 according to the present embodiment. The storage device 911 may include a storage medium, a recording device that records data on the storage medium, a reading device that reads data from the storage medium, a deletion device that deletes data recorded on the storage medium, and the like. The storage device 911 stores programs executed by the CPU 901 and various data.
The drive 912 is a storage medium reader / writer, and is built in or externally attached to the information processing apparatus 10. The drive 912 reads information recorded on a mounted removable storage medium such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, and outputs the information to the RAM 903. The drive 912 can also write information on a removable storage medium.
The communication device 915 is a communication interface configured with, for example, a communication device for connecting to the network N. In particular, the communication device 915 according to the embodiment of the present disclosure includes a wireless LAN (Local Area Network) compatible communication device. The communication device 915 may include an LTE (Long Term Evolution) compatible communication device and a wire communication device that performs wired communication.
The network N is a wired or wireless transmission path for information transmitted from a device connected to the network N. For example, the network N may include a public line network such as the Internet, a telephone line network, a satellite communication network, various LANs (Local Area Network) including Ethernet (registered trademark), a WAN (Wide Area Network), and the like. Further, the network N may include a dedicated network such as an IP-VPN (Internet Protocol-Virtual Private Network).
The hardware configuration illustrated in FIG. 45 is merely an example, and the hardware configuration illustrated in FIG. 45 is not limited as long as the operation of the information processing apparatus 10 according to the embodiment of the present disclosure described above can be realized.
In addition, it is possible to create a program for causing hardware such as a CPU, ROM, and RAM incorporated in a computer to exhibit functions equivalent to those of the above-described information processing apparatus. A computer-readable storage medium that records the program can also be provided.
An image acquisition unit for acquiring an image including a plurality of objects;
A display control unit that receives an operation on the first object among the plurality of objects and controls identification information indicating the second object associated with the second object;
The information processing apparatus according to (1), wherein the display control unit controls the identification information based on an identification result of the first object that has undergone an operation on the first object.
The information processing apparatus according to (2), wherein the operation on the first object is an operation of designating a region corresponding to the first object in the image.
The information processing apparatus according to (1), wherein the operation on the first object is an operation of setting identification information indicating the first object.
An identification information acquisition unit that acquires one or more candidates of identification information indicating the second object;
The display control unit displays an candidate estimated from the one or more candidates as the identification information in response to an operation on the first object. The information processing apparatus according to one item.
The display control unit displays one or more candidates of identification information indicating the second object in association with the second object, receives an operation on the first object, and receives the one or more candidates. The information processing apparatus according to (5), wherein the identification information indicating the second target object is updated based on the identification information indicating the candidate estimated from the candidates.
The identification information acquisition unit acquires one or more candidates of identification information indicating the first object,
Upon receiving designation of one candidate from one or more candidates of identification information indicating the first object, the identification information indicating the second object is based on the specified one candidate, The information processing apparatus according to (5) or (6), wherein the information processing apparatus is estimated from one or more candidates of identification information indicating two objects.
The identification information acquisition unit acquires one or more candidates of identification information indicating the target object extracted from the target object included in each of a plurality of regions set in the image. The information processing apparatus according to any one of (7).
The information processing apparatus according to (8), wherein the identification information acquisition unit acquires a candidate of identification information indicating the target object extracted based on the identification result of the target object included in the region.
The identification information acquisition unit acquires a candidate of identification information indicating the target object, which is extracted based on the identification result of the target object included in the updated area when the area is updated, The information processing apparatus according to (9).
The information processing apparatus according to any one of (8) to (10), wherein at least one area among the plurality of areas is specified based on an image analysis process on the image.
The object is a dish,
The information processing apparatus according to (11), wherein the at least one region is specified based on a recognition result of the container in which the dish is served.
The information processing apparatus according to (11), wherein the at least one region is specified based on a recognition result of the person's face.
The information processing apparatus according to any one of (5) to (13), wherein the identification information acquisition unit acquires one or more candidates for the identification information from an external server.
The identification information indicating the second object is estimated based on a co-occurrence probability indicating a probability that an object indicated by each of one or more candidates of the identification information co-occurs with the first object, The information processing apparatus according to any one of (5) to (14).
The information processing apparatus according to any one of (5) to (15), wherein the display control unit displays the accuracy of an estimation result of identification information indicating the second object so as to be identifiable.
The information processing apparatus according to any one of (1) to (16), including an imaging unit that captures the image.
A terminal including a display unit;
A server including an image acquisition unit that acquires an image including a plurality of objects, and a display control unit that displays the image on the display unit;
The information processing system, wherein the display control unit receives an operation on the first object among the plurality of objects and controls identification information indicating the second object associated with the second object.
Acquiring an image including multiple objects;
Receiving an operation on the first object among the plurality of objects, controlling identification information indicating the second object associated with the second object;
The storage medium which memorize | stored the program which performs.
DESCRIPTION OF SYMBOLS 1, 1 'Information processing system 10 Information processing apparatus 20 User terminal 102 Image acquisition part 104 Object extraction part 106 Object specification part 108 Accuracy specification part 110 Feature-value data storage part 112 Identification information designation part 114 Extraction part 116 Co-occurrence probability Data storage unit 118 Estimation unit 120 Display control unit 122 Update unit 202 Imaging unit 204 Operation unit 206 Display unit
The information processing apparatus according to claim 1, wherein the display control unit controls the identification information based on an identification result of the first object that has received an operation on the first object.
The information processing apparatus according to claim 2, wherein the operation on the first object is an operation of designating a region corresponding to the first object in the image.
The information processing apparatus according to claim 1, wherein the operation on the first object is an operation for setting identification information indicating the first object.
The information processing apparatus according to claim 1, wherein the display control unit is configured to display a candidate estimated from the one or more candidates as the identification information in response to an operation on the first object.
The display control unit displays one or more candidates of identification information indicating the second object in association with the second object, receives an operation on the first object, and receives the one or more candidates. The information processing apparatus according to claim 5, wherein the identification information indicating the second object is updated based on the identification information indicating the candidate estimated from the candidates.
Upon receiving designation of one candidate from one or more candidates of identification information indicating the first object, the identification information indicating the second object is based on the specified one candidate, The information processing apparatus according to claim 5, wherein the information processing apparatus is estimated from one or more candidates of identification information indicating two objects.
The said identification information acquisition part acquires one or more candidates of the identification information which shows the said object extracted about the said object contained in each of the some area | region set in the said image, The said identification information acquisition part is acquired. Information processing device.
The information processing apparatus according to claim 8, wherein the identification information acquisition unit acquires a candidate of identification information indicating the target object extracted based on the identification result of the target object included in the region.
The identification information acquisition unit acquires candidates for identification information indicating the target object extracted based on the identification result of the target object included in the updated area when the area is updated. Item 10. The information processing device according to Item 9.
The information processing apparatus according to claim 8, wherein at least one area among the plurality of areas is specified based on an image analysis process on the image.
The information processing apparatus according to claim 11, wherein the at least one region is specified based on a recognition result of the container in which the dish is served.
The information processing apparatus according to claim 11, wherein the at least one region is specified based on a recognition result of the person's face.
The information processing apparatus according to claim 5, wherein the identification information acquisition unit acquires one or more candidates for the identification information from an external server.
The identification information indicating the second object is estimated based on a co-occurrence probability indicating a probability that each of the objects indicated by one or more candidates of the identification information co-occurs with the first object. Item 6. The information processing device according to Item 5.
The information processing apparatus according to claim 5, wherein the display control unit displays the accuracy of an estimation result of identification information indicating the second object so as to be identifiable.
The information processing apparatus according to claim 1, further comprising an imaging unit that captures the image.
JP2013130504A 2013-06-21 2013-06-21 Information processing device, information processing system, and storage medium storing program Pending JP2015005172A (en)
JP2013130504A JP2015005172A (en) 2013-06-21 2013-06-21 Information processing device, information processing system, and storage medium storing program
US14/280,766 US9531955B2 (en) 2013-06-21 2014-05-19 Information processing device, information processing system, and storage medium storing program
JP2015005172A true JP2015005172A (en) 2015-01-08
ID=52110626
JP2013130504A Pending JP2015005172A (en) 2013-06-21 2013-06-21 Information processing device, information processing system, and storage medium storing program
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