Patent Publication Number: US-9836799-B2

Title: Service provision program

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application is based upon and claims the benefit of priority of the prior Japanese Patent Application No. 2014-136746, filed on Jul. 2, 2014, the entire contents of which are incorporated herein by reference. 
     FIELD 
     The embodiments discussed herein are related to a service provision program. 
     BACKGROUND 
     Recently, development is progressing in methods for executing image processing on images captured by cameras and the like, automatically detecting the occurrence of situations that differ from normal in the captured image, and determining whether or not a specific subject is included in the captured image. Such image determinations generally employ a method of comparing a feature amount in a captured image and in an image of the subject serving as a reference for determination (determination-use image). 
     However, for example, for captured images such as those captured outside, the image capture conditions of the captured image sometimes differ from the image capture conditions of the determination-use image, such as by changes in the brightness of light illuminating the subject to be determined. The detection precision and the precision of determination of the subject are therefore lowered in abnormal situations. 
     Hitherto, reference data for feature amounts of the captured images have therefore first been obtained from a relationship between predetermined brightness data and a feature amount of the captured image, based on brightness data of the captured images. A monitoring device has been proposed that then automatically detects the occurrence of situations that differ from normal captured images, even if the captured image is captured in conditions of changed illumination, by comparing the difference between obtained reference data and the feature amount obtained from the captured image. 
     RELATED PATENT DOCUMENTS 
     Japanese Patent Application Laid-Open (JP-A) No. H09-322151 
     SUMMARY 
     According to an aspect of the embodiments, a program generates a modified image by executing modification processing in a computer on an image of a mark affixed to a product. The program executes to provide the generated modified image as a determination-use image employable in determination as to whether or not the product affixed with the mark is included in a captured image. 
     The object and advantages of the invention will be realized and attained by means of the elements and combinations particularly pointed out in the claims. 
     It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are not restrictive of the invention. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a diagram illustrating an example of a captured image. 
         FIG. 2  is a diagram illustrating an example of a service provision system. 
         FIG. 3  is a diagram illustrating an example of processing that determines image similarity. 
         FIG. 4  is a diagram illustrating an example of a database structure for a determination-use image. 
         FIG. 5  is a diagram illustrating an example of a service provision device implemented by a computer. 
         FIG. 6  is flowchart illustrating an example of a flow of determination-use image generation processing of a service provision system according to a first exemplary embodiment. 
         FIG. 7  is a diagram illustrating an example of modification processing. 
         FIG. 8  is a diagram illustrating an example of modification processing. 
         FIG. 9  is flowchart illustrating an example of a flow of determination processing of a service provision system according to a first exemplary embodiment. 
         FIG. 10  is a flowchart illustrating an example of a flow of determination-use image generation processing of a service provision system according to a second exemplary embodiment. 
         FIG. 11  is a diagram illustrating an example of modification processing that rotates a 3D model of a product. 
         FIG. 12  is a diagram illustrating an example of modification processing performed on a 3D model of a product. 
         FIG. 13  is a flowchart illustrating an example of a flow of determination processing of a service provision system according to the second exemplary embodiment. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     Detailed explanation follows regarding an example of an exemplary embodiment according to technology disclosed herein, with reference to the drawings. 
     Recently, social networking services (SNS) that share information between somewhat interlinked communities are being extensively used as a community instrument for individuals and corporations. In SNS, information is spread within a community using text, images, audio, etc., and this is becoming recognized as an important advertising medium for corporate enterprises. At the same time, opportunities are increasing to implement campaigns using SNS and aimed at promoting sales of products. 
     In the present exemplary embodiment, an example of a system is explained that determines whether or not a product sold by a given manufacturer is included in an image when, for example, a user posts an image captured of the product to the SNS, and then provides a gift or the like to the user that posted the image including the product. Although the type of product made the subject of a campaign is not limited, explanation follows of an example in which a product A that is a canned drink sold by a drinks manufacturer serves as the product subject to a campaign. 
     In implementing such a campaign, there is a need to determine whether or not a mark representing the product A is included in a captured image posted to an SNS by a user, such as that illustrated in  FIG. 1 . As is apparent from  FIG. 1 , the product A is sometimes captured from various angles, and since the mark of the product A is displayed along the side face of the can, imaging sometimes takes place without the whole mark being imaged due to actions such as holding the can in the hand, resulting in a state in which a portion of the mark is omitted. 
     Moreover, when, for example, a drinks manufacturer also provides gifts to users who post images to an SNS depicting an advertising flag displaying a mark of the product A, the mark of the product A will sometimes be imaged in a warped state since the shape of the flag changes under the influence of, for example, the wind. Such warping of the mark also sometimes occurs when the lens of the camera that captured the image is mounted with a wide-angle lens or a fisheye lens. 
     Since various states are imaged in this manner even for the same mark, it is often difficult to determine whether or not the mark of the product A is included in the captured image. 
     Although the product A is surrounded by a rectangle to make the position of the product A in the captured image clear in the example of  FIG. 1 , there is no rectangle surrounding the product A in the captured image posted to the SNS by the user. The mark discussed here may be any mark as long as it is a mark representing the product A, such as a mark identifying the manufacturer of that sells the product A, a mark identifying the product A, or a campaign mark. The mark may be text, may be a graphic design, or may be a single mark in which text and a graphic design are combined. 
     Recently, software capable of processing captured images in various ways is being provided in information devices such as smart phones, and opportunities are therefore increasing for users to post captured images that have been image processed on SNSs. 
     Image processing includes various processes performed on an original captured image, such as image rotation, resizing, color modification, image blurring, background elimination, and effect processing that enhances a specific impression by changing the impression of an image. Note that image blurring is also known as airbrushing. 
     Specific examples of effect processing include processing that processes an image into a comic book style by outline extraction, and processing that processes an image into a toy camera style by blurring the whole image, making the brightness of the peripheral portions of the image lower than the brightness of an image center portion, and setting the saturation and contrast higher than the actual values. Other examples of effect processing include various image processes performed in order to draw out a specific impression effect, such as vintage styling that processes a sepia tone image to give an impression of passage of time, and processing in which an image is processed into a pop art style that emphasizes contrast differences. 
     Thus even if a captured image includes the mark of the product A, it is often difficult to determine whether or not the mark of the product A is included in the captured image when image processing has been performed on the captured image. 
     Hence the following exemplary embodiment explains a system that generates determination-use images for determining with high precision whether or not a predetermined subject is included in various captured images in which capture conditions may differ, and on which image processing may have been performed. 
     First Exemplary Embodiment 
       FIG. 2  is a diagram illustrating an example of a service provision system  10  according to an exemplary embodiment. 
     The service provision system  10  is a system in which a service provision device  20 , a user terminal  30 , a social networking service server  40  (referred to as SNS server  40  hereafter), and a service request terminal  50  are connected to one another through a communication line  60 . Note that although the communication line  60  according to the present exemplary embodiment is an internet line in the following explanation, the type of the communication line  60  is not limited thereto. For example, the communication line  60  may be a dedicated line, or may be an intranet such as a company-wide LAN. The architecture of the communication line  60  may be formed using any out of wires, wireless, or a mixture of wires and wireless. 
     The user terminal  30  is a terminal that posts captured images of the product A taken by the user to an SNS. The user terminal  30  is, for example, a personal computer (PC), a notebook PC, a tablet terminal, or a smartphone, and may be any information device that includes a function for sending captured image data to the SNS server  40  through the communication line  60 . 
     The SNS server  40  is a server that stores captured images sent from the user terminal  30 , and manages the publication destination and the like of the captured image based on a security policy set by the user who sent the captured image. 
     The service request terminal  50  is, for example, installed at the drinks manufacturer implementing the campaign for the product A, and exchanges information needed in the campaign for the product A with the service provision device  20  described below. A browser pre-installed to the service request terminal  50  may be employed for data exchange with the service provision device  20 , or a dedicated application provided by the administrator managing the service provision device  20  may be employed. The installation location of the service request terminal  50  is not limited to within the corporate entity implementing the campaign, and it goes without saying that the service request terminal  50  may be installed at any location depending on the situation. 
     The user terminal  30 , the SNS server  40 , and the service request terminal  50  may each have plural connections to the communication line  60 . 
     The service provision device  20  includes a communications section  21 , a provision section  22 , a determination section  23 , and a document database  24 . The document database  24  is simply referred to as database  24  hereafter. 
     The communications section  21  connects the service provision device  20  to the communication line  60 , and exchanges the data that is needed with information devices such as the user terminal  30 , the SNS server  40 , and the service request terminal  50 . 
     Specifically, the communications section  21  includes a reception section  25  and an output section  26 . The reception section  25  receives data from the user terminal  30 , the SNS server  40 , and the service request terminal  50 . The output section  26  outputs data included in the service provision device  20  as necessary to the user terminal  30 , the SNS server  40 , and the service request terminal  50 . 
     For example, the drinks manufacturer transmits images of the mark of the product A from the service request terminal  50  to the reception section  25  of the service provision device  20  over the communication line  60  in preparation for the campaign for the product A. Images of the mark of the product A transmitted to the service provision device  20  from the service request terminal  50  are referred to as “registered images” to distinguish them from other images. The registered images transmitted from the service request terminal  50  to the service provision device  20  may be either still images or video. The registered images in the present exemplary embodiment are still images as an example. 
     The reception section  25  is capable of receiving from the service request terminal  50  information related to the product A such as the color, size, shape, material, and applications of the product A, and additional information including instructions for modification processing to be executed on the registered images. The shape of the product A is information indicating, for example, whether the shape of the product A appears spherical or cylindrical when viewed. The material of the product A is information indicating whether the surface of the product A is, for example, metallic or plastic. The applications of the product A is information related to, for example, situations for using the product A, such as content related to main locations for usage, main time periods of usage, and portability, indicating whether or not the product A can be carried. The instructions for modification processing to be executed on the registered images are, for example, instructions for image processing such as blurring processing or background elimination to be executed on the registered images. 
     The service request terminal  50  does not need to transmit every item of additional information mentioned above to the reception section  25 . For example, the service request terminal  50  may transmit just the shape of the product A to the reception section  25 . 
     The provision section  22  includes a generation section  27 . The generation section  27  generates modified images by executing modification processing on the registered images received by the reception section  25 . 
     Specifically, the generation section  27  includes in advance various image processing algorithms for executing image rotation, resizing and the like, and for example, generates modified images of the registered images by executing image processing on the registered images based on the additional information received by the reception section  25 . 
     The generation section  27  then uses a known feature extraction algorithm to calculate feature amounts of the registered images and of the generated modified images. 
     Binary Robust Invariant Scalable Keypoints (BRISK) is employed as an example of a feature extraction algorithm in the generation section  27  according to the present exemplary embodiment; however, the feature extraction algorithm employed by the generation section  27  is not limited thereto. For example, when additional information is received indicating that the product A is often used in a tilted state, a feature extraction algorithm that excels in extracting features of images including objects that are tilted may be employed. 
     Note that BRISK is a feature extraction algorithm in which scale invariability and rotation invariability are introduced to Binary Robust Independent Elementary Features (BRIEF), which are focused on differences in luminance between two separated points of an image. BRISK extracts feature amounts from images having modified scale and rotation with higher precision than Scale Invariant Feature Transform (SIFT) or Speeded Up Robust Feature (SURF) that are typical feature extraction algorithms for extracting from images feature amounts that are invariant on scaling and rotation. 
       FIG. 3  is a diagram illustrating an example of processing that determines the similarity between the registered images and modified images processed by the generation section  27 , based on the feature amounts extracted from each of the registered images and the modified images. 
     A feature amount a of a modified image extracted using a feature extraction algorithm is expressed as a vector quantity ^a=[a0, . . . , aN], and a feature amount b of a registered image prior to modification processing is expressed as a vector quantity ^b=[b0, . . . , bN]. Herein, N+1 (0≦N&lt;∞) represents the dimensionality of the feature amount, and the ^ symbol denotes a vector. 
     The generation section  27  calculates distances between extracted feature amounts extracted from two respective images, and determines that the registered image and the modified image are similar images when the value of the calculated distance is a predetermined threshold value (similarity determination threshold value S1) or lower. In the generation section  27  according to the present exemplary embodiment, for example, the Euclid distance, as expressed by L2 distance indicated in Equation (1), is employed as a distance of feature amounts representing image similarity. 
     
       
         
           
             
               
                 
                   
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     Herein, dL 2 (^a, ^b) denotes an L2 distance between the feature amount a and the feature amount b. Accordingly, the similarity between the registered image and the modified image increases as the value of dL 2 (^a, ^b) decreases. 
     The determination of similarity between images is not limited to methods employing the L2 distance. For example, the similarity between the registered image and the modified image may be determined by mapping feature amounts extracted from each image at feature points, which are at locations to which feature extraction was applied, to vector space, and then comparing the vector distribution of the feature points of each image using statistical methods, machine learning, or the like. 
     Any indicator capable of quantitatively indicating the extent of similarity between images, such as the distance Lp indicated in Equation (2), may be employed in the determination of extent of similarity between images. 
     
       
         
           
             
               
                 
                   
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     Herein, dL p (^a, ^b) denotes a distance Lp between the feature amount a and the feature amount b. 
     When the calculated L2 distance is the similarity determination threshold value S1 or less, namely, when the registered image and the modified image are similar, the generation section  27  then performs image processing on the registered image until the registered image and the modified image are no longer similar. 
     However, when the calculated L2 distance is greater than the similarity determination threshold value S1, namely, when the registered image and the modified image are dissimilar, the generation section  27  ends modification processing on the registered image. 
     The generation section  27  thereby generates modified images such that the differences in feature amount of the respective images from the registered images are more than the similarity determination threshold value S1. 
     The provision section  22  stores the modified images generated by the generation section  27  in the database  24 , described below, as determination-use images employable in the determination as to whether or not the product A affixed with the mark represented by the registered images is included in the captured image posted to the SNS server  40  by the user, and provides the determination-use images to the determination section  23 . 
     The determination-use image for the product A that was provided by the provision section  22  is stored in the database  24 . The database  24  employs a document database, typically MongoDB for example. MongoDB is a document database that includes plural documents in respective collections, and can define freely selected fields for each document according to the structure and data format of the determination-use image. MongoDB is a database applied for management of so-called big data, in which database distribution as the amount of stored determination-use images increases is relatively simple compared to in a conventional relational database (RDB). 
       FIG. 4  is a diagram illustrating an example of a database structure a determination-use image. Note that any text following “//” in  FIG. 4  is a comment explaining the content of the corresponding field. 
     The database  24  according to the present exemplary embodiment includes, for example, a primary key, a time of storage, photographer information, information regarding the imaged object, information regarding the feature extraction algorithm, the feature amount in the image, the determination algorithm of the modified image generation, information regarding effects applied to the image, image storage location, and the like. 
     Although the database  24  according to the present exemplary embodiment employs MongoDB in order to store determination-use images, the employed database is not limited thereto, and other types of databases may be employed. Moreover, a known file system such as the NT File System (NTFS) may be employed without employing a database. 
     The reception section  25  receives captured images posted to the SNS server  40  during the campaign period for the product A. 
     The determination section  23  receives captured images from the reception section  25  and extracts feature amounts from the captured images using the same feature extraction algorithm as the generation section  27 . The determination section  23  then, for example, calculates an L2 distance indicating the extent of similarity of the image from the feature amounts of the captured image and the feature amounts of the determination-use images of the product A stored in the database  24  using Equation (1). The determination section  23  then determines whether or not the product A affixed with the mark represented by the registered images is included in the captured image by comparing the calculated L2 distance and the similarity determination threshold value S1. 
     According to the determination result made by the determination section  23 , the output section  26  outputs an email or the like containing information related to the campaign for the product A, such as a URL link to an application form for a gift, to the user who posted the captured image including the product A for example. The reception section  25  may acquire the captured image managed by the SNS server  40 , and information such as an email address specifying the provider of the captured image, using application programming interfaces (API) pre-prepared for each SNS service. 
       FIG. 5  illustrates a computer system  100  as a computer implementable example of the service provision device  20 , the user terminal  30 , the SNS server  40 , and the service request terminal  50  included in the service provision system  10 . 
     The computer system  100  illustrated in  FIG. 5  as the service provision system  10  includes a computer  200  as the service provision device  20 . The computer system  100  also includes a computer  300  as the user terminal  30 , a computer  400  as the SNS server  40 , and a computer  500  as the service request terminal  50 . 
     The computer  200  includes a CPU  202 , memory  204 , and a nonvolatile storage section  206 . The CPU  202 , the memory  204 , and the nonvolatile storage section  206  are connected to one another through a bus  208 . The computer  200  includes an input section  210  such as a keyboard and mouse, and a display section  212  such as a display. The input section  210  and the display section  212  are connected to the bus  208 . The computer  200  also includes an IO  214  for reading/writing from/to a recording medium  232 , and the IO  214  is connected to the bus  208 . The computer  200  also includes a communications interface (IF) as an interface for connecting to the communication line  60 , and the communications IF  216  is also connected to the bus  208 . The storage section  206  may be implemented by a hard disk drive (HDD), flash memory, or the like. 
     A service provision program  218  that causes the computer  200  to function as the service provision device  20  illustrated in  FIG. 2 , a similarity determination information storage region  228 , and a database storage region  230  are stored in the storage section  206 . The service provision program  218  stored in the storage section  206  includes a communications process  220 , a provision process  222 , and a determination process  224 . 
     The CPU  202  reads the service provision program  218  from the storage section  206 , expands the service provision program  218  into the memory  204 , and executes each process included in the service provision program  218 . The CPU  202  expands similarity determination information included in the similarity determination information storage region  228  into the memory  204  as the similarity determination threshold value S1. The CPU  202  expands information for configuring a database included in the database storage region  230  into the memory  204 , and configures the database  24 . 
     The computer  200  operates as the service provision device  20  illustrated in  FIG. 2  by the CPU  202  reading the service provision program  218  from the storage section  206 , expanding the service provision program  218  into the memory  204 , and executing the service provision program  218 . The computer  200  operates as the communications section  21  including the reception section  25  and the output section  26  illustrated in  FIG. 2  by the CPU  202  executing the communications process  220 . The computer  200  operates as the provision section  22  included in the generation section  27  illustrated in  FIG. 2  by the CPU  202  executing the provision process  222 . The computer  200  operates as the determination section  23  illustrated in  FIG. 2  by the CPU  202  executing the determination process  224 . 
     Note that the service provision device  20  may also be implemented by, for example, a semiconductor integrated circuit, and more specifically by an application specific integrated circuit (ASIC), or the like. 
     Next, explanation follows regarding operation of the service provision device  20  according to the present exemplary embodiment. When determination-use images of the product A are not stored in the database  24 , the service provision device  20  according to the present exemplary embodiment executes determination-use image generation processing. 
       FIG. 6  is a flowchart illustrating an example of a flow of the determination-use image generation processing according to the present exemplary embodiment. 
     First, at step S 10 , initialization processing that is needed to execute the determination-use image generation processing is executed. Specifically, the generation section  27  acquires the similarity determination threshold value S1 expanded in to the memory  204 . 
     At step S 20 , the reception section  25  determines whether or not the registered images of the product A were received from service request terminal  50 . In cases of negative determination, processing of step S 20  is repeated until the registered images are received. In cases of affirmative determination, the received registered images and reproductions of the registered images are stored in a predetermined region in the memory  204 . When additional information is appended to the registered images, the reception section  25  also associates the registered images with the additional information, and stores the registered images and the additional information in a predetermined region in the memory  204 . 
     At step S 30 , the generation section  27  executes modification processing on the registered images received in the processing of step S 20 . In cases in which there is additional information associated with the registered images, the content of the modification processing executed on the registered images is determined based on the content of the additional information. 
     For example, in a case of additional information indicating that the product A is cylindrical in shape, has metal as the material, is carried in the hand, and is mainly used outdoors, the generation section  27  determines that the mark of the product A tends to be imaged in a state in which a portion thereof is omitted since the product A is cylindrical in shape and is used while being held. The generation section  27  also determines that the mark of the product A tends to be imaged in a state in which light is reflected since the additional information indicates that the material of the product A is metal, and the product A is mainly used outdoors. 
     As a result of the above determination, the generation section  27  performs image processing on the registered images received in the processing of step S 20  such that the registered images appear to be curved along the side face of a cylinder, and have a portion thereof omitted. The generation section  27  then performs further effect processing on the registered images on which image processing has been performed, to produce a hue and brightness corresponding to being in sunlight. 
     The generation section  27  is provided in advance with a correspondence table stipulating correspondences between content specified by the additional information, and modification processing to be executed on the registered images when content is specified. The generation section  27  determines the contents of the modification processing to be executed on the registered images by referencing the correspondence table. There are no limitations to the content of the modification processing executed on the registered images by the generation section  27 , and various known modification processing may be executed. 
     For example, the brightness and hue of the product A change as the amount of light and the wavelength of the light illuminating the product A change with the type of light source illuminating the product A. Therefore the weather during image capture, or the type of light source, such as sunlight, a fluorescent light, or a candle, is sometimes specified in the additional information. In such cases, the generation section  27  references the correspondence table, and performs modification processing corresponding to the light source that illuminates the surroundings of the product A, namely, corresponding to information related to an ambient light source, on the registered images. 
       FIG. 7  is a diagram illustrating an example of modification processing executed by the generation section  27  on the registered images. The generation section  27  executes various types of modification processing on the registered images according to the specified content of the additional information, such as blurring, background elimination, warping, processing to add highlights, processing according to the weather or ambient lighting, processing to give the characteristics of a distinctive camera such as a toy camera, pop art styling, comic book styling, and vintage styling. 
     At step S 40 , the generation section  27  extracts from each image the feature amount ^a of the modified image generated by the processing of step S 30  using the BRISK method, and the feature amount ^b of the reproduction of the registered image stored in the memory  204  by the processing of step S 20 . 
     The generation section  27  then calculates the L2 distance dL 2  (^a, ^b) indicating the extent of similarity between the images according to Equation (1) from the feature amounts ^a of the modified image and the feature amounts ^b of the reproduction of the registered image. 
     At step S 50 , determination is made as to whether or not the L2 distance dL 2  (^a, ^b) calculated by the processing of step S 40  is greater than the similarity determination threshold value S1 acquired by the processing of step S 10 , namely, whether or not the modified image is dissimilar to the registered image. 
     Herein, similarity between a modified image and a registered image denotes that even by using the modified image, determination precision is still only obtained at an equivalent level to the determination precision when the registered image prior to the modification processing is used to determine whether or not the product A is included in a captured image posted to the SNS. As described above, sometimes the image capture conditions for the captured image posted to the SNS differ, and various types of image processing are applied to the captured image. Therefore determination as to whether or not the product A is included in the captured image cannot be made with high precision with modified images that are similar to the registered images but do not take these issues into consideration. 
     Thus, when the determination of the current step is a negative determination, namely, when the modified image and the registered image are similar, processing transitions to step S 60 . Then, at step S 60 , the generation section  27  further executes modification processing according to the content of the additional information on the modified image generated by the processing of step S 30 , and generates a modified image in which the extent of modification is increased compared to the modified image generated by the processing of step S 30 . Processing then transitions to step S 40 , and the generation section  27  repeats the processing of step S 40  to step S 60  to generate a modified image having a feature amount differing from the feature amounts of the registered images by more than the similarity determination threshold value S1. 
     However, processing transitions to step S 70  when affirmative determination is made by the determination processing of step S 50 . At step S 70 , the provision section  22  saves the modified image determined by the determination processing of step S 50  to be dissimilar to the registered images as a determination-use image in a predetermined region in the memory  204 . 
     This thereby completes the determination-use image generation processing. 
     When additional information is not appended to the registered images received in the processing of step S 20 , the generation section  27  may perform predetermined modification processing according to the general features common to the type of the product A on the registered images. For example, if the product A is a canned drink, considering that the registered images will generally be depicted on the side face of a cylinder, and that it is rare to have situations in which imaging captures the whole of the registered images, modification processing may be executed to give missing portions of the registered images. 
     In the processing of step S 30  modification processing is executed on the registered images based on the content of the additional information associated with the registered images. However, pre-processing that executes predetermined modification processing such as background elimination may be performed on the registered images, and modification processing based on the content of the additional information may be executed on the registered images after finishing pre-processing. The modification processing executed by the pre-processing is not limited to background elimination. For example, modification processing may be performed that causes the feature amounts of the registered images to be extracted more accurately than in cases in which pre-processing is not performed. 
       FIG. 8  is a diagram illustrating an example of a flow that generates a determination-use image after pre-processing has been performed on a registered image. In the example illustrated in  FIG. 8 , the background of the registered image depicting a mark of a plane is eliminated by pre-processing, and the determination-use image is generated by executing each type of processing of step S 30  onward on the registered image from which the background has been eliminated. However, in the processing of step S 50 , the image whose feature amount is to be compared against that of the modified image generated by the processing of step S 30  is the registered image after pre-processing, rather than the registered image received in the processing of step S 20 . 
     In such cases, since temporarily modifying the registered image using the pre-processing sometimes causes the feature amount of the registered image to be more accurately extracted than in cases in which the pre-processing is not performed, the difference in the feature amount to the modified image can be more accurately determined in the determination processing of step S 50 . Namely, the extent of similarity between registered images and the modified image can be more accurately determined than in cases in which pre-processing is not executed, and modified images that are dissimilar to the registered images can be set as determination-use images. Determination as to whether or not the product A is included in a captured image posted to the SNS can accordingly be determined with higher precision using determination-use images generated in this manner. 
       FIG. 9  is a flowchart illustrating an example of a flow of determination processing executed by the service provision device  20  during the campaign period for the product A, after the determination-use image generation process as illustrated in  FIG. 6  has ended. 
     First, at step S 100 , the reception section  25  references the SNS server  40  and determines whether or not there is a captured image posted to the SNS server  40 . When negative determination is made, the processing of step S 100  is repeated and the SNS server  40  is continuously referenced. In cases of affirmative determination, one of the captured images is acquired from the SNS server  40  and stored in a predetermined region in the memory  204 , and processing transitions to step S 110 . When doing so, the reception section  25  uses an API, provided by the administrator or the like managing the SNS server  40 , to acquire identification information uniquely indicating the user who posted the acquired captured image, for example an email address, and stores the identification information in the memory  204  in association with the captured image. 
     At step S 110 , the determination section  23  acquires the determination-use images of the product A from the database  24  along with feature amounts of the determination-use images. 
     At step S 120 , the determination section  23 , for example, performs image matching by scanning the captured images while resizing the determination-use images acquired by the processing of step S 110 . The determination section  23  then uses the same feature extraction algorithm as the feature extraction algorithm used by the generation section  27  to calculate the feature amounts of the regions of the captured images determined to be most similar to the determination-use images. 
     The determination section  23  then calculates, for example according to Equation (1), the L2 distance indicating the extent of mutual image similarity from the feature amount of the captured image extracted in the current step and from the feature amounts of the determination-use images acquired by the processing of step S 110 . Although the L2 distances between the captured images and the determination-use images are calculated here, an indicator of the extent of image similarity may be calculated other than the L2 distance. 
     Then, at step S 130 , the determination section  23  acquires the similarity determination threshold value S1 expanded in the memory  204 , and determines whether or not the L2 distance calculated at step S 120  is the similarity determination threshold value S1 or less. Processing transitions to step S 150  when the determination result of the current step is an affirmative determination, namely, when determination is made that there is a region in the captured image similar to a determination-use images for the product A. 
     At step S 150 , the determination section  23  determines that an image of the product A is included in the captured image since a portion of the captured image and a determination-use image for the product A are similar to each other. 
     Then, at step S 160 , the determination section  23  requests that the output section  26  output an email containing information relating to the campaign for the product A such as a URL link to an application form for a gift to the user who uploaded the captured image. The output section  26  generates the email based on the request from the determination section  23  and outputs the generated email to the email address that was acquired by the processing of step S 100  of the user who posted the captured image, and the determination processing illustrated in  FIG. 9  ends. 
     When negative determination is made in the determination processing of step S 130 , processing transitions to step S 140 . Then, at step S 140 , the determination section  23  determines that no image of the product A is included in the captured image, and the determination processing illustrated in  FIG. 9  ends. 
     The determination processing illustrated in  FIG. 9  is repeatedly executed during the campaign period for the product A. 
     As a result of the determination processing, the user that received the email from the service provision device  20  may, for example, obtain a reward such as a gift from the drinks manufacturer by accessing the URL in the email and inputting the mandatory information into the application form. 
     Although an email is sent to the user who posted the captured image each time determination is made that an image of the product A is included in a captured image in the determination processing illustrated in  FIG. 9 , the timing at which the email is sent is not limited thereto. For example, the email may be saved in the memory  204 , and the email may be sent to the user who posted the captured image after the campaign period for the product A ends. 
     Moreover, the service provision device  20  may output information identifying the determination result made by the determination section  23  and the provision source of the captured image to the service request terminal  50  of the drinks manufacturer implementing the campaign for the product A. In such cases, customer information for the product A may be collected by the drinks manufacturer using the SNS. 
     In this manner, based on the additional information including the color, size, outline shape, material, usage, and the like of the product A, the service provision device  20  according to the present exemplary embodiment is able to generate a determination-use image according to the tendency of image capture conditions of the product A included in the captured image, and the tendency to perform image processing on the captured image. Moreover, even when there is no additional information, determination-use images can be generated according to the tendency of the image capture conditions of the product A included in the captured image, and the tendency to perform image processing on the captured image, by executing predetermined modification processing on the registered images according to general features common to the type of the product A. Moreover, the determination-use images are subjected to modification processing such that the feature amounts differ from the feature amounts of the registered images by more than the similarity determination threshold value S1. Accordingly, determination as to whether or not the product A is included in a captured image can be made with higher precision than in cases in which registered images are employed as determination-use image as they are without performing modification processing. 
     Second Exemplary Embodiment 
     Next, explanation follows regarding a second exemplary embodiment. The service provision system according to the second exemplary embodiment is similar to the service provision system  10  according to the first exemplary embodiment illustrated in  FIG. 2 . The service provision system according to the second exemplary embodiment can therefore by implemented by a similar computer system to the computer system  100  according to the first exemplary embodiment illustrated in  FIG. 5 . 
     However, the service provision device  20  of  FIG. 2  is read as a service provision device  20 A, the generation section  27  of  FIG. 2  is read as a generation section  27 A, the determination section  23  of  FIG. 2  is read as a determination section  23 A, and the provision section  22  of  FIG. 2  is read as a provision section  22 A. Moreover, the computer  200  of  FIG. 5  is read as a computer  200 A, the provision process  222  of  FIG. 5  is read as a provision process  222 A, the determination process  224  of  FIG. 5  is read as a determination process  224 A, and the service provision program  218  of  FIG. 5  is read as a service provision program  218 A. 
     Herein, the same reference numerals are allocated to portions corresponding to those of the first exemplary embodiment, and explanation focuses on the portions that differ from the first exemplary embodiment. 
     According to the present exemplary embodiment, in addition to the additional information described in the first exemplary embodiment, the service provision device  20 A also receives 3D model data of the product A appended to the registered images from the service request terminal  50 . The service provision device  20  then forms a 3D model of the product A based on the 3D model data, and generates modified images in which the registered images are affixed to the formed 3D model. 
     The generation section  27 A therefore includes known algorithms for generation of the 3D model and for performing image processing on the 3D model, such as rotation, and other processing. 
     Next, explanation follows regarding operation of the service provision device  20 A according to the present exemplary embodiment. The service provision device  20 A according to the present exemplary embodiment executes the determination-use image generation processing when determination-use images for the product A are not stored in the database  24 .  FIG. 10  is a flowchart illustrating an example of a flow of determination-use image generation processing according to the present exemplary embodiment. 
     First, at step S 200 , initialization processing needed to execute the determination-use image generation processing is executed. Specifically, the generation section  27 A acquires the similarity determination threshold value S1 expanded in to the memory  204 . 
     At step S 210 , the reception section  25  determines whether or not the registered images of the product A and the additional information including the 3D model data of the product A were received from service request terminal  50 . In cases of negative determination, the processing of step S 210  is repeated until the registered images and the additional information are received. In cases of affirmative determination, the registered images received from the reception section  25  are saved in a predetermined region in the memory  204 , and the registered images and the additional information are associated with each other and stored in a predetermined region in the memory  204 . 
     At step S 220 , the generation section  27 A forms the 3D model of the product A from the 3D model received by the processing of step S 210 . The generation section  27 A then performs image processing to affix the registered images received by the processing of step S 210  over the surface of the formed 3D model formed of the product A. The position where the registered images are affixed on the 3D model may, for example, be specified as additional information from the service request terminal  50 . The generation section  27 A then generates, as modified images, images when the 3D model is viewed from predetermined positions. 
     A modified image  1  illustrated in  FIG. 11  is an image illustrating an example of a modified image generated by the current step. The modified image  1  illustrated in  FIG. 11  represents a modified image in which the mark “ABC”, which is a registered image, is affixed to the surface of a 3D model representing a sphere. 
     At step S 230 , the generation section  27 A saves the modified images generated at step S 220  to a predetermined region of the memory  204  as reference images. 
     Then, at step S 240 , the generation section  27 A rotates the 3D model by a predetermined amount in any direction and generates, as a modified image, an image of the 3D model, as viewed from the same position as the position from which the 3D model was viewed in the processing of step S 220 . The rotation direction and rotation amount of the 3D model may, for example, be specified as additional information from the service request terminal  50 , and when not specified in the additional information, the generation section  27 A may rotate the 3D model in a predetermined direction by a predetermined rotation amount. 
     Although the rotation amount of the modified image is not particularly limited, it is preferable to set the rotation amount as small as possible since this enables more modified images to be generated. 
     A modified image  2  illustrated in  FIG. 11 , is a modified image generated by rotating the 3D model corresponding to modified image  1  toward the left side of the drawing. The display position of the registered image “ABC” changes with the rotation of the 3D model, since position from which the 3D model is viewed is fixed. 
     At step S 250 , the generation section  27 A determines whether or not the 3D model has been rotated one full turn from the position corresponding to the reference image set by the processing of step S 230 . The generation section  27 A can determine whether or not the 3D model has rotated a full turn based on the cumulative rotation amount from the position corresponding to the reference image set by the processing of step S 230 . A modified image M illustrated in  FIG. 11  is a modified image representing a state after the 3D model has rotated a full turn. Processing transitions to step S 260  when the determination processing of the current step makes a negative determination. 
     At step S 260 , the generation section  27 A acquires the feature amounts ^a of the rotated modified images generated by the processing of step S 240 , and the feature amounts ^b of the currently set reference images, using, for example, the BRISK method. 
     The generation section  27 A then calculates the L2 distance dL 2  (^a, ^b) representing the extent of similarity between images, for example according to Equation (1), from the feature amounts ^a of the rotated modified images and the feature amounts ^b of the reference images. 
     At step S 270 , the generation section  27 A determines whether or not the L2 distance dL 2  (^a, ^b) calculated at step S 260  is greater than the similarity determination threshold value S1 acquired by the processing of step S 200 , namely, determines whether or not the rotated modified image is dissimilar to the reference image. Similarity between the rotated modified image and the reference image means that the rotated modified image generated by the processing of step S 240  can be determined as being the product A when the existing reference images are employed. 
     Accordingly, when negative determination is made, namely, when the rotated modified image and the reference image are similar, processing transitions to step S 240  without setting the current rotated modified image as a new reference image. 
     However, processing transitions to step S 280  when the affirmative determination has been made by the determination processing of step  270 . The rotated modified image being dissimilar to the reference image means that the feature amounts of the product A is not able to be extracted from the rotated modified image representing the product A from a different angle, even though the image depicts the same product A. 
     Thus at step S 280 , instead of just the existing reference images, the generation section  27 A sets the rotated modified image determined as being dissimilar to the existing reference images as a new reference image, and processing transitions to step S 240 . The processing of step S 240  to step S 280  is then repeated to generate reference images of the product A in which the feature amounts of the images mutually differ by more than the similarity determination threshold value S1, while rotating the 3D model of the product A. 
     Processing transitions to step S 290  when the determination processing of step S 250  makes an affirmative determination, namely, when the 3D model of the product A has rotated a full turn. 
     At step S 290 , the provision section  22 A saves all of the reference images generated by the generation section  27 A in the processing of step S 230  and step S 280  in a predetermined region of the memory  204 , as determination-use images. There will then be at least one or more determination-use images present since a reference image is generated by the processing of step S 230 . 
     This thereby completes the determination-use image generation processing. 
     In this manner, the determination-use image generation processing according to the present exemplary embodiment is able to generate determination-use images simulating the product A as viewed from plural angles by rotating the 3D model. Moreover, the number of determination-use images can be reduced since the determination-use images are dissimilar to one another. 
     In the determination-use image generation processing according to the present exemplary embodiment, an example has been given in which the 3D model is rotated in one direction; however, determination-use images may be generated in which the product A is viewed from plural direction while rotating the 3D model in various directions. Such cases obtain determination-use images in which the product A is displayed from various angles, thereby enabling determination-use images to be generated in which captured images including the product A are extracted with higher precision than when the determination-use images generated by rotating the 3D model in one direction. 
     In the determination-use image generation processing according to the present exemplary embodiment, images of the whole of the 3D model of the product A affixed with the registered image are generated as determination-use images; however, just the registered images on which modification processing has been executed to give the different display angles that accompany rotation of a 3D model may be generated as determination-use images. 
     However, employing images of the 3D model of the product A as determination-use images enables determination to be made as to whether or not the product A is included in a captured image from not only the mark of the product A, but also from the shape of the product A. This accordingly enables determination as to whether or not the product A that is the determination subject is included in a captured image posted to the SNS to be made with higher precision than when just the registered images on the 3D model are employed as the determination-use images. 
     Similarly to the processing of step S 30  of the first exemplary embodiment illustrated in  FIG. 6 , in the processing of step S 220  of the determination-use image generation processing according to the present exemplary embodiment, modification processing may be performed on the 3D model of the product A affixed with the registered image according to the content of the additional information. 
       FIG. 12  is a diagram illustrating an example of modification processing performed on the 3D model of the product A affixed with the registered image. As illustrated in  FIG. 12 , for example, modification may be made for an ambient light source of a specified type hitting the 3D model from a position specified by the additional information, modifying the impression of the surface of the 3D model according to the material specified by the additional information. Modified images of the product A on which such modification processing has been executed enable determination-use images to be generated that extract the product A included in a captured image with higher precision, since they are more similar to images of the product A included in the captured images. 
     In the determination-use image generation processing according to the present exemplary embodiment, at the processing of step S 280  the reference images for comparison with the rotated modified image are successively updated when the difference that is the distance between the feature amounts of the rotated modified image and feature amounts of the reference image are greater than the similarity determination threshold value S1. However, the determination-use images may be generated by each time comparing against the reference images set by the processing of step S 230 , without updating the reference images being compared to the rotated modified images. 
     In such cases, in the processing of step S 280 , the generation section  27 A, for example, stores rotated modified images determined to not be similar to the existing reference images in the memory  204 . The generation section  27 A then changes the value of the similarity determination threshold value S1 employed in the determination processing of step S 70  each time the processing of step S 80  is executed. For example, if the value of similarity determination threshold value S1 is provisionally set as K for generating determination-use images with image feature amounts that different from one another by more than similarity determination threshold value S1, each time the processing of step S 280  is executed, the value of the similarity determination threshold value S1 may be changed in the sequence K, (K×2), (K×3), and so on. 
     The provision section  22 A may then, in the processing of step S 290 , set the reference images set by the processing of step S 30  and the rotated modified images saved in the memory  204  by the processing of step S 280  as determination-use images. 
     An example has been given in the determination-use image generation processing according to the present exemplary embodiment in which 3D model data of the product A is received from the service request terminal  50 . However, the 3D model data may be pre-prepared by the service provision device  20 A, and the 3D model data to be employed may be selected from the service request terminal  50 . Such cases enable a reduction in the effort by the manufacturer to prepare the 3D model data of the product A. 
     In the determination-use image generation processing according to the present exemplary embodiment, the generation section  27 A rotates the 3D model and generates the determination-use images of the product A viewed from plural angles after generating the 3D model of the product A affixed with the registered image. However, the method of generating determination-use images of the product A viewed from plural angles is not limited thereto. 
     For example, real-time images may be received that have been sent from a terminal (image capture terminal) installed with a dedicated application provided by the administrator that manages the service provision device  20 , and captured while moving through one turn around the periphery of the product A. Note that the dedicated application may include a function that sends images from during image capture to the service provision device  20  in real-time, and, for example, the service request terminal  50  may be employed as the image capture terminal. A video may be configured to include plural frames appended with frame numbers in ascending time-sequence order of capture of the real-time images, and by assembling the images corresponding to each of the frames. 
     In such cases the reception section  25  receives instructions from the image capture terminal for image capture start and image capture end, and starts acquisition of the real-time images according to the instruction to start image capture. Then, after setting as a reference image the image of FRAME  1  acquired from the real-time images, the generation section  27 A acquires images of FRAME  2  onward in sequence one frame at a time, and compares the feature amounts of images of the acquired frames against the feature amounts of the reference image. The generation section  27 A then sets the image of the acquired frame as a new reference image if the feature amounts differ from each other by more than the similarity determination threshold value S1. After this, the generation section  27 A may determine the difference in feature amounts between the image of the next acquired frame and the new reference image, and repeatedly execute processing to extract new reference images from the real-time images until an instruction to end image capture is received. The provision section  22 A then provides each of the reference images obtained in this manner as determination-use images. 
     The generation section  27 A may compare the feature amounts of each of the frame images after executing the specified modification processing on each frame image according to the additional information. 
       FIG. 13  is a flowchart illustrating an example of a flow of determination processing executed during the campaign period for the product A by the service provision device  20  after the determination-use image generation processing illustrated in  FIG. 10  has ended. 
     First, at step S 300 , the reception section  25  references the SNS server  40  and determines whether or not there is a captured image posted to the SNS server  40  by a user. The processing of step S 300  is repeated and the SNS server  40  is continuously referenced in cases of negative determination. In cases of affirmative determination, the captured images are acquired from the SNS server  40  and stored in a predetermined region in the memory  204 , and processing transitions to step S 310 . When doing so, the reception section  25  uses an API, provided by the administrator or the like managing the SNS server  40 , to acquire identification information uniquely indicating the user who posted the acquired captured image, for example an email address, and stores the identification information in the memory  204  in association with the captured image. 
     At step S 310 , the determination section  23 A determines whether or not there are any determination-use images not yet acquired from the determination-use images of the product A stored in the database  24 , and processing transitions to step S 330  when affirmative determination is made. 
     At step S 330 , the determination section  23 A acquires a determination-use image not yet acquired from the database  24  along with a feature amount of the determination-use image. 
     Then, at step S 340 , the determination section  23 A, for example, performs image matching by scanning the captured images received in the processing of step S 300  while resizing the determination-use images acquired by the processing of step S 330 . The determination section  23 A then uses the same feature extraction algorithm as the feature extraction algorithm used by the generation section  27 A to calculate the feature amounts of the regions of the captured images determined to be most similar to the determination-use images. 
     The determination section  23 A then calculates, for example according to Equation (1), the L2 distance indicating the extent of mutual image similarity from the feature amounts of the captured image calculated in the current step and from the feature amounts of the determination-use images acquired by the processing of step S 330 . 
     At step S 350 , the determination section  23 A acquires the similarity determination threshold value S1 expanded in the memory  204 , and determines whether or not the L2 distance calculated at step S 340  is the similarity determination threshold value S1 or less. In cases of affirmative determination, namely, cases in which determination is made that there are no regions in the captured image similar to the determination-use images of the product A, processing transitions to step S 310 , and the processing that determines whether or not there is a region in the captured images similar to another determination-use image of the product A is repeated. Processing transitions to step S 360  when the determination result of the current step is an affirmative determination, namely, when determination is made that there is a region in the captured image similar to a determination-use image for the product A. 
     At step S 360 , the determination section  23 A determines that the product A is included in the captured image since a portion of the captured image and a determination-use image for the product A are similar to each other. 
     Then, at step S 370 , the determination section  23 A requests that the output section  26  output an email containing information relating to the campaign for the product A, such as a URL link to an application form for a gift, to the user who uploaded the captured image. The output section  26  generates the email based on the request from the determination section  23 A and outputs the generated email to the email address that was acquired by the processing at step S 300  of the user who posted the captured image, and the determination processing illustrated in  FIG. 13  ends. 
     When negative determination is made in the determination processing of step S 310 , namely, when determination is made that there are no regions in the captured image similar to any of the determination-use images for the product A, processing transitions to step S 320 . Then, at step S 320 , the determination section  23 A determines that the product A is not included in the captured image, and the determination processing illustrated in  FIG. 13  ends. 
     In this manner, in the determination processing according to the present exemplary embodiment, determination processing is executed using determination-use images of the product A displayed from plural angles generated by the determination-use image generation processing illustrated in  FIG. 10 . Accordingly, determination as to whether or not the product A subject to determination is included in a captured image can be made with higher precision than when determination processing is executed using a determination-use image displayed from a single angle. 
     Although explanation of technology disclosed herein has been given above using exemplary embodiments, technology disclosed herein is not limited to the scope of the exemplary embodiments above. Various modifications and improvements may be made to the exemplary embodiments above within a range not departing from the spirit of technology disclosed herein, and the technological scope of technology disclosed herein also encompasses modes in which such modifications and improvements have been made. For example, the processing sequence may be modified within a range not departing from the spirit of technology disclosed herein. 
     Although explanation has been given of exemplary embodiments in which the service provision programs  218 ,  218 A are pre-stored (pre-installed) on the storage section  206 , there is not limitation thereto. The service provision programs according to technology disclosed herein may also be provided in a format recorded on a computer readable recording medium. For example, the service provision programs according to technology disclosed herein may also be provided in a format recorded on a portable recording medium such as a CD-ROM, a DVD-ROM, or USB memory. The service provision programs according to technology disclosed herein may also be provided in a format recorded on, for example, semiconductor memory such as flash memory. 
     In the first exemplary embodiment and the second exemplary embodiment, explanation has been given regarding an example in which the service provision devices  20 ,  20 A are applied to a sales promotion campaign for a product using the SNS, and determination is made as to whether or not the product that is the campaign subject is included in images posted to the SNS. 
     However, the areas of application of the service provision devices  20 ,  20 A are not limited to this example. 
     For example, the service provision devices  20 ,  20 A may be employed in a marketing service utilizing the images posted to the SNS. 
     To simplify the explanation below, explanation is given of an example situation in which the service provision device  20  employs a marketing service. However, the service provision device  20 A may also be applied in a similar situation. 
     First, the service provision device  20  executes the determination-use image generation processing illustrated in  FIG. 6 , and generates a determination-use image for the product (product subject to analysis) that is the subject of the marketing service. Note that there may be plural products subject to analysis, and determination-use images are generated for each of the products subject to analysis in such cases. 
     The service provision device  20  then acquires the captured image posted to the SNS server  40 , and determines whether or not the product subject to analysis is included in captured image by executing the determination processing illustrated in  FIG. 9 . 
     When the product subject to analysis is included into the captured image, information related to when the product subject to analysis was imaged is also acquired in addition to the captured image. The content of the related information acquired is not limited, and includes information acquirable from the captured image. 
     For example, the related information may include the number of products subject to analysis included in the captured image, the ratio of the area occupied by products subject to analysis with respect to the size of the captured image, the image capture environment indicating whether the image capture location was indoors or outdoors, the weather at the time of image capture, and the like. Moreover, the type of processing for treating the captured image may be acquired, such as sepia toning such that an image exhibits the impression of passage of time, or styling as an illustration. Moreover, information may be acquired that indicates the number of people depicted with the product subject to analysis, their estimated ages, and whether they are smiling or angry, and information related to fashion that indicates whether they are formally dressed or casually dressed. 
     The relation information may also include information related to the image matching executed by the processing of step S 120  illustrated in  FIG. 9  and step S 340  illustrated in  FIG. 13 . The information related to the image matching is, for example, information indicating which location of the captured image was scaled resized and by what extent, and to which determination-use image it was determined to be similar. 
     Then, based on the acquired related information related to the product subject to analysis, the service provision device  20 , for example, performs analysis of information indicating who tends to use the product subject to analysis, and when and where they tend to use it, with this information being valuable in sales promotion of the product subject to analysis and the like. The analysis results are then sent to the service request terminal  50  of the manufacturer that requested the marking service. 
     When this is performed, the service provision device  20  may send the related information to the service request terminal  50  along with the analysis results. Moreover, the related information acquired from the captured images may be sent to the service request terminal  50  alone, and analysis based on the related information may implemented in the service request terminal  50 . 
     When the product subject to analysis is analyzed based on the related information, the service provision device  20  may analyze a combination of other information such as text posted to the SNS server  40  together with the captured image, and marking data related to the product subject to analysis provided by the manufacturer. 
     In this manner, the service provision device  20  is able to provide the manufacturer with information related to sales promotion of the product. 
     Explanation follows regarding an example situation in which the service provision devices  20 ,  20 A are employed in an active support service employing the images posted to the SNS. The active support service includes, for example, a service that analyzes images that include the product, and forwards images that are images for analysis that give a favorable reaction to many people, namely, images likely to prove popular, and thereby increases favorable feelings toward the manufacturer by users. 
     The active support service determines whether or not the product subject to active support (the product subject to support) is included in the captured image by a method similar to that of the marking service described above. 
     When the product subject to support is included in the captured image, analysis is made as to whether or not the captured image is likely to be a popular image. In this analysis, for example, analysis is performed using plural evaluation items such as the feelings of people depicted in the captured image, the presence of absence of animals, and the content of any modification processing performed on the captured image, and sets a score for each evaluation item. For example, captured images in which the depicted people are laughing are set with a higher score than those in which the people are angry. The scores for each evaluation item are then summed, and captured images with a predetermined score or greater are determined to be images likely to be popular. 
     In such cases, captured images likely to be popular out of images depicting the product of the manufacturer that requested the active support service can be spread across the internet. This enables the service provision device  20  to provide the manufacture with a service for increasing the favorable feelings toward the manufacturer by users. 
     Explanation has been given in the exemplary embodiments of cases in which the service provision devices  20 ,  20 A are implemented by single computers  200 ,  200 A. However, the processes may be executed by different computers, and the service provision devices  20 ,  20 A may be implemented by respective computers connected by the communication line  60 , in a distributed processing configuration. 
     In such cases, the provision processes  222 ,  222 A provide the determination-use images over the communication line  60  to the computers that execute the determination processes  224 ,  224 A, respectively. 
     Although the captured images are acquired from the SNS server  40  in each exemplary embodiment, the acquisition source of the captured images is not limited to the SNS server  40 , and may be a server where users of an unspecified large number of user terminals  30  connected to the communication line  60  publish captured images, such as a message board or home page. 
     Although the first exemplary embodiment and the second exemplary embodiment adopt modes in which the registered images are received from the manufacturer, a request may be received from the manufacturer, and the registered images may be prepared by the service provider that manages the service provision devices  20 ,  20 A. 
     As described above, software capable of conveniently executing various types of processing and correction on captured images is provided to information devices such as smartphones, and the occurrence of captured images in which processing such as elimination of the background of the captured image is increasing. 
     Conventionally, the appearance of a subject included in captured images changes according to image capture conditions during image capture of the subject, such as the angle and exposure amount, and image processing performed on the captured image, even though the captured images are of the same subject, and the subject is sometimes mis-recognized as not being included in a captured image. 
     One aspect of technology disclosed herein exhibits an advantage effect of enabling determination as to whether or not a subject is included in a captured image to be made with higher precision. 
     All examples and conditional language provided herein are intended for the pedagogical purposes of aiding the reader in understanding the invention and the concepts contributed by the inventor to further the art, and are not to be construed as limitations to such specifically recited examples and conditions, nor does the organization of such examples in the specification relate to a showing of the superiority and inferiority of the invention. Although one or more embodiments of the present invention have been described in detail, it should be understood that the various changes, substitutions, and alterations could be made hereto without departing from the spirit and scope of the invention.