Patent Publication Number: US-8533196-B2

Title: Information processing device, processing method, computer program, and integrated circuit

Description:
TECHNICAL FIELD 
     The present invention relates to technology for attaching, to a content, information on characteristics of the content. 
     BACKGROUND ART 
     In recent years, thanks to the spread of digital still cameras, digital video cameras, and personal computers (PCs), individual users can now store a vast amount of digital content, such as still images and video. 
     Furthermore, technology such as film scanning has made it easy to digitalize analog content, such as photographs taken with a silver salt camera or video taken with an 8 mm camera, and store the digitalized content in a PC or the like. 
     The need thus arises for easy management of a large quantity of content stored in a location such as a PC. One possibility is for management based on the shooting date of the content or the shooting location. In the case of content captured with a digital still camera or a digital video camera, accompanying data such as the shooting date or shooting location can be acquired from Exchangeable Image File Format (Exif) data attached to the content. Information such as the shooting date or shooting location cannot be acquired, however, from digitalized content originally captured with a silver salt camera or an 8 mm camera. Furthermore, some digital still cameras and digital video cameras with low functionality do not have a Global Positioning System (GPS) function. In this case, accompanying data on the shooting location cannot be acquired. 
     To address this problem, Patent Literature 1 discloses technology to analyze a content and estimate the accompanying data that should be attached to the content. In Patent Literature 1, a plurality of similar registered images are extracted, the similar registered images having image feature amounts similar to the image feature amounts for an input image (content). Shared items are then extracted from among accompanying information for the plurality of similar registered images. If a shared item satisfies a predetermined condition, the shared item is set as accompanying data for the input image. 
     CITATION LIST 
     Patent Literature 
     
         
         Patent Literature 1: Japanese Patent Application Publication No. 2005-275847 
       
    
     SUMMARY OF INVENTION 
     Technical Problem 
     With the technology disclosed in Patent Literature 1, however, the accompanying data to be attached to contents is estimated separately for each content, thus making it difficult to attach the same accompanying data to a plurality of related contents. For example, a user may travel to a certain region and consider the trip an event, managing a plurality of contents captured in that region by attaching accompanying data indicating the event. With the technology in Patent Literature 1, each of the contents captured at tourist attractions in the region have attached thereto, as accompanying data, information indicating the tourist attraction at which the content was captured. It is thus not possible to attach shared accompanying data, which treats the trip as an event, to the plurality of contents captured during the trip. 
     In light of the above problems, it is an object of the present invention to provide an information processing device, a processing method, a computer program, and an integrated circuit that attach shared accompanying data to a plurality of contents. 
     Solution to Problem 
     In order to achieve the above object, the present invention is an information processing device comprising: a first acquisition unit operable to acquire a plurality of contents; a second acquisition unit operable to acquire one or more characteristics of each acquired content and to search among a plurality of reference data items in order to acquire one or more reference data items that each include any of the one or more characteristics, each reference data item having attached thereto at least one attribute used for identifying the reference data item, the reference data items being data other than the plurality of contents; a specification unit operable to specify, among the attributes attached to the acquired one or more reference data items, an attribute that also pertains to the plurality of contents, the specified attribute being used for identifying each of the plurality of contents; and an attachment unit operable to attach the specified attribute to each of the plurality of contents. 
     Advantageous Effects of Invention 
     With the above structure, the information processing device uses the attributes attached to the reference data items to specify a shared attribute that is to be used to identify the plurality of contents and attaches the specified attribute to each of the plurality of contents. Therefore, the information processing device can attach a shared attribute to a content group composed of a plurality of contents. By attaching the shared attribute to each of the plurality of contents, the information processing device can, for example, search the content group using the attached attribute as a keyword for searching. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a block diagram showing the structure of an information processing device  10 . 
         FIG. 2  shows an example of the data structure of a database  103 . 
         FIG. 3  is a flowchart outlining processing by the information processing device  10 . 
         FIG. 4  is a flowchart of operations for classification by the information processing device  10 . 
         FIG. 5  is a flowchart of operations for estimation of accompanying data by the information processing device  10 . 
         FIG. 6  shows a specific example of estimating accompanying data related to location using non-fixed accompanying data. 
         FIG. 7  shows a specific example of estimating accompanying data related to location using fixed accompanying data. 
         FIG. 8  shows a specific example of estimating accompanying data related to time period using fixed accompanying data. 
         FIG. 9  is a flowchart showing estimation of accompanying data when estimating the accompanying data for contents targeted for estimation using a plurality of accompanying data items. 
         FIG. 10  shows a specific example of estimating accompanying data related to time period using a plurality of accompanying data items. 
         FIG. 11  is a block diagram showing the structure of an information processing device  1000 . 
         FIG. 12  is a block diagram showing the structure of an information processing device  1000   a.    
         FIG. 13  is a block diagram showing the structure of an information processing device  1000   b.    
         FIG. 14  is a block diagram showing the structure of an information processing device  1000   c.    
         FIG. 15  is a block diagram showing the structure of an information processing device  2000 . 
         FIG. 16  shows an example of the data structure of a database  2103 . 
         FIG. 17  is a flowchart outlining processing by the information processing device  2000 . 
         FIG. 18  is a flowchart of operations for estimation of accompanying data by the information processing device  2000 . 
         FIG. 19  shows a specific example of estimating accompanying data related to time period using dictionary data. 
         FIG. 20  is a block diagram showing the structure of an information processing device  3000 . 
         FIG. 21  shows an example of the data structure of a database  3103 . 
         FIG. 22  is a flowchart outlining processing by the information processing device  3000 . 
         FIG. 23  is a flowchart showing operations for age estimation object acquisition. 
         FIG. 24  is a flowchart showing operations for time period estimation. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     The following describes Embodiments of the present invention with reference to the Figures. 
     1. Embodiment 1 
     The following describes an information processing device  10  in Embodiment 1 of the present invention. 
     1.1 Structure 
     As shown in  FIG. 1 , the information processing device  10  includes a content storage unit  101 , a content group selection unit  102 , a database  103 , an object selection unit  104 , a content analysis unit  105 , a similar object specification unit  106 , an accompanying data extraction unit  107 , an accompanying data estimation unit  108 , an accompanying data writing unit  109 , and a receiving unit  110 . 
     (1) Database  103   
     The database  103  is located in the information processing device  10 . The database  103  stores digital content data managed by the content storage unit  101  and accompanying data for the digital content data, as well as feature amounts of objects included in the digital content data. Furthermore, the database  103  also stores digital content data that does not have any other information attached thereto. 
       FIG. 2  shows an example of the internal structure of the database  103 . As shown in  FIG. 2 , the database  103  stores digit content  201 , . . . ,  202 , and  301 ,  302 , . . . ,  303  in a content storage area. 
     In this context, a digital content refers to information stored in a digital format. For example, a digital content may be a still image photographed with a digital still camera or a video filmed with a digital video camera. 
     The digital contents  201 , . . . ,  202  include, for each digital content, feature amount data on the entire content, feature amount data on objects included in the digital content, and accompanying data for the digital content. For example, the digital content  201  is composed of digital content data A 210 , which is image data; feature amount data  211  on the entire content; a feature amount data set  212  composed of feature amount data for objects A 1 , A 2 , . . . , Aa; and an accompanying data set  213  composed of accompanying data A 1 , A 2 , . . . , Ab. The objects A 1 , A 2 , . . . , Aa are objects detected within the digital content data A 210 . An object refers to a person, thing, or landscape included in one image of a digital content, or any region extracted from an image. Feature amounts refer to the hue or brightness of an image in a digital content, constituent elements of the image such as contours, and a variety of characteristics calculated based on the constituent elements of the image. 
     The accompanying data A 1 , A 2 , . . . , Ab is attribute information for a digital content and is attached to the digital content. For example, accompanying data may be Exif data attached to an image photographed by a digital still camera, or metadata such as a keyword or a tag attached to a digital content at a digital content upload site. The accompanying data A 1 , A 2 , . . . , Ab is also used to identify the digital content. For example, if accompanying data indicating the date of creation is attached to each digital content, such accompanying data is used to identify a digital content created on a specific date. 
     The digital contents C  301 , D  302 , . . . , E  303  are contents not yet associated with feature amounts of the entire content, feature amounts of objects, and accompanying data. 
     The information processing device  10  uses the digital contents  201 , . . . ,  202  to identify the accompanying data that is to be associated with the contents not yet associated with feature amounts of the entire content, feature amounts of objects, and accompanying data. Hereinafter, contents not yet associated with feature amounts of the entire content, feature amounts of objects, and accompanying data are referred to as contents targeted for estimation. On the other hand, digital contents that have been associated with feature amounts of the entire content, feature amounts of objects, and accompanying data are referred to as stored contents. 
     (2) Content Storage Unit  101   
     The content storage unit  101  stores, in the database  103 , contents managed by the information processing device  10 , such as digital contents associated with accompanying data and the like (the digital contents  201 , . . . ,  202  in  FIG. 2 ) and contents targeted for estimation (the digital contents  301 ,  302 , . . . ,  303  in  FIG. 2 ). The content storage unit  101  manages the contents stored in the database  103 . 
     (3) Content Group Selection Unit  102   
     The content group selection unit  102  extracts a plurality of contents targeted for estimation from among the digital contents managed by the content storage unit  101 . Based on user operation, the content group selection unit  102  classifies each of the extracted contents targeted for estimation into one or more input content groups. 
     An input content group refers to a collection of one or more contents targeted for estimation. One method of classification is to group together a plurality of contents targeted for estimation based on indices such as date, event, or location, as indicated by user operation. Specifically, contents may be classified as belonging to a predetermined time frame starting from a certain date and time, or as being within a predetermined range from a certain position. 
     (4) Object Selection Unit  104   
     The object selection unit  104  selects one or more objects as input objects for each content targeted for estimation included in the input content group. 
     Specifically, the object selection unit  104  receives a designation via the receiving unit  110 , the designation being provided by user operation, of a region (such as a rectangular region) that includes an object in the content targeted for estimation. Upon receiving the designation, the object selection unit  104  selects the designated region and object. The selected object is hereinafter referred to as an input object. 
     (5) Content Analysis Unit  105   
     The content analysis unit  105  analyzes the one or more input objects selected by the object selection unit  104  and calculates feature amounts for each input object. 
     Note that methods of calculating feature amounts are well-known technology, and therefore a description of such methods is omitted here. For example, such technology is disclosed in Japanese Patent Application Publication No. 2001-84274 and Japanese Patent Application Publication No. 2005-275847. 
     (6) Similar Object Specification Unit  106   
     For each input object, the similar object specification unit  106  extracts one or more of a plurality of objects that are included in digital contents other than the input content group, i.e. included in a plurality of stored contents (such objects hereinafter being referred to as “stored objects”), and that are similar to the input object (such objects hereinafter being referred to as “similar objects”). The similar object specification unit  106  extracts the one or more similar objects using the calculated feature amounts and the feature amounts associated with each of the stored objects. 
     (7) Accompanying Data Extraction Unit  107   
     For each of the similar objects extracted for each input object, the accompanying data extraction unit  107  extracts all of the accompanying data associated with the stored contents that include the similar object. 
     (8) Accompanying Data Estimation Unit  108   
     The accompanying data estimation unit  108  estimates the most appropriate accompanying data for each input content group based on the accompanying data extracted by the accompanying data extraction unit  107 . 
     The method of estimating accompanying data is based on the number or type of accompanying data items for the similar contents used for estimation, or on whether the accompanying data is fixed or non-fixed. Fixed accompanying data refers to accompanying data having predetermined subject matter, such as Exif data used in images photographed by a digital still camera. Non-fixed accompanying data refers to accompanying data whose subject matter is not predetermined, such as a keyword or tag that is attached to an image at a digital content upload site. 
     Specifically, the accompanying data estimation unit  108  receives, via the receiving unit  110 , a type of accompanying data to be used for estimation, as selected by user operation from among a plurality of accompanying data items. Upon receiving the type of accompanying data, the accompanying data estimation unit  108  uses accompanying data corresponding to the received type in order to estimate, for each input content group, one or more accompanying data items to be associated with all of the contents targeted for estimation in the input content group. The type of accompanying data is, for example, the creation date included in Exif data, Global Positioning System (GPS) information, or information distinguishing a keyword or tag included in metadata. 
     Specifically, the accompanying data estimation unit  108  extracts accompanying data that is shared by all of the input objects selected from the contents targeted for estimation in the input content group. The accompanying data estimation unit  108  then designates the extracted accompanying data as the accompanying data to be associated with each of the contents targeted for estimation. The accompanying data estimation unit  108  may create combinations of the extracted accompanying data in order to designate all of the extracted accompanying data as the accompanying data to be associated with each of the contents targeted for estimation. Note that a specific example of estimating accompanying data for the input content group is described below. 
     (9) Accompanying Data Writing Unit  109   
     The accompanying data writing unit  109  associates one or more accompanying data items estimated by the accompanying data estimation unit  108  for each input content group with all of the contents targeted for estimation in the corresponding input content group. 
     (10) Receiving Unit  110   
     The receiving unit  110  is, for example, a remote control, mouse, keyboard, or the like for a user to provide input to the information processing device  10 . 
     The receiving unit  110  receives a variety of instructions via user operation. 
     For example, regarding a plurality of contents targeted for estimation that have been extracted, the receiving unit  110  receives instructions regarding the input content group to which each of the contents targeted for estimation belongs. 
     The receiving unit  110  also receives a designation of an object included in a content targeted for estimation. 
     Furthermore, the receiving unit  110  receives a type of accompanying data to be used by the accompanying data estimation unit  108 . 
     1.2 Operations 
     (1) Outline of Operations 
     The following describes an outline of operations by the information processing device  10  to estimate accompanying data for an input content group with reference to the flowchart in  FIG. 3 . 
     The content group selection unit  102  classifies one or more contents targeted for estimation into one or more input content groups based on user operation (step S 5 ). 
     The object selection unit  104  selects one or more input objects for each content targeted for estimation included in each input content group (step S 10 ). Specifically, the object selection unit  104  receives a designation, provided by user operation, of a region (such as a rectangular region) that includes an object in the content targeted for estimation. Upon receiving the designation, the object selection unit  104  selects the designated region as an input object. 
     The content analysis unit  105  calculates feature amounts for each of the one or more input objects selected by the object selection unit  104  (step S 15 ). Specifically, the content analysis unit  105  analyzes each of the one or more input objects selected in step S 10  and calculates the feature amounts for the input object. 
     For each of the input objects extracted in step S 10 , the similar object specification unit  106  extracts one or more stored objects (similar objects) that are similar to the input object from among one or more stored objects included in one or more stored contents (step S 20 ). The determination of whether a stored object is similar to an input object is made by comparing the feature amounts of an input object extracted in step S 15  with the feature amounts of a stored object included in a stored content. It is then determined whether the two sets of feature amounts match, or if the difference is within an allowable range. When the feature amounts match or are within an allowable range, the two objects are determined to be similar. A predetermined value or a user-set value may be used as the allowable range. 
     For each of the one or more similar objects extracted in step S 20 , the accompanying data extraction unit  107  extracts accompanying data of the stored content that includes the similar object (step S 25 ). 
     The accompanying data estimation unit  108  uses the accompanying data extracted in step S 25  to estimate accompanying data for each input content group (step S 30 ). 
     For each input content group, the accompanying data writing unit  109  records the accompanying data estimated in step S 30  for all of the contents targeted for estimation included in the input content group (step S 35 ). 
     (2) Classification 
     The following describes the classification of step S 5  in  FIG. 3  in detail with reference to the flowchart in  FIG. 4 . 
     Based on user operation, the content group selection unit  102  selects contents targeted for estimation from among the digital contents stored in the database  103  (step S 100 ). 
     Once contents targeted for estimation are selected in step S 100 , the content group selection unit  102  classifies the selected contents targeted for estimation into one or more input content groups (step S 105 ). 
     (3) Estimation of Accompanying Data 
     The following describes the estimation of accompanying data in step S 30  of  FIG. 3  in detail with reference to the flowchart in  FIG. 5 . Note that this estimation process is performed for each content targeted for estimation. 
     Via the receiving unit  110 , the accompanying data estimation unit  108  receives a user indication of a type of accompanying data to use for estimation from among the accompanying data of stored contents extracted in step S 25  of  FIG. 3  (step S 200 ). 
     The accompanying data estimation unit  108  determines whether the received type of accompanying data is fixed (step S 205 ). Specifically, the accompanying data estimation unit  108  determines whether the received type of accompanying data is fixed based on the storage region, for a stored content, of accompanying data corresponding to the received type of accompanying data. This is because accompanying data and non-fixed accompanying data are stored in different regions. 
     If the type of accompanying data is not fixed, i.e. if the type of accompanying data is determined to be non-fixed (step S 205 : No), the accompanying data estimation unit  108  uses one or more stored contents for each input content group to extract accompanying data, or a fraction of accompanying data, that matches the received type of accompanying data (step S 210 ). 
     After performing step S 210 , or when it has been determined that the received type of accompanying data is fixed (step S 205 : YES), the accompanying data estimation unit  108  selects or combines the fixed accompanying data, or the accompanying data or a fraction thereof extracted in step S 210 , in order to generate the accompanying data estimated as having the highest probability of being the most appropriate accompanying data for the input content group (step S 215 ). 
     1.3 Specific Example 
     The following describes a specific example of estimating accompanying data for contents targeted for estimation. 
     (1) Specific Example 1 
     First, a specific example of using non-fixed accompanying data to estimate accompanying data regarding location is described. 
       FIG. 6  shows extracted stored content and corresponding non-fixed accompanying data (keywords) for an input content group as a result of the information processing device  10  starting estimation of accompanying data related to location by performing processing from step S 5  through step S 25  in  FIG. 3 . 
     The following is a detailed description of the steps through which the accompanying data for the stored contents shown in  FIG. 6  is obtained. In step S 5  of  FIG. 3 , a photograph of “Tokyo ABC Tower” (contents targeted for estimation C 100 ) and a photograph of a train on the “Tokyo AA Train Line” (contents targeted for estimation C 101 ) are classified into an input content group. 
     Next, in step S 10 , a region including the “Tokyo ABC Tower” (hereinafter, OBJ 1 ) and a region including the train on the “Tokyo AA Train Line” (hereinafter, OBJ 2 ) are selected as input objects in the respective contents targeted for estimation C 100  and C 101 . 
     Then, in step S 15 , the feature amounts of OBJ 1  and OBJ 2  are calculated. In step S 20 , three similar objects having feature amounts near the calculated feature amounts are extracted for each input object. Stored contents 1-1, 1-2, and 1-3 are extracted for OBJ 1 , whereas stored contents 2-1, 2-2, and 2-3 are extracted for OBJ 2 . 
     In step S 25 , the accompanying data shown in  FIG. 6  is obtained from the stored contents 1-1 through 2-3, which include the extracted similar objects. In this state, accompanying data estimation (step S 30 ) begins. 
     The accompanying data estimation unit  108  then performs step S 200 . Since the obtained accompanying data is of one type (keyword only), the accompanying data is used as is for estimation. 
     Next, the accompanying data estimation unit  108  performs step S 205 . The obtained accompanying data is a keyword, which is non-fixed. Processing thus proceeds to step S 210 . The accompanying data estimation unit  108  extracts accompanying data, or a fraction of accompanying data, that matches the type of accompanying data to be estimated, i.e. location. In this case, “Tokyo” is extracted as a keyword from “Trip to Tokyo” and “Tokyo ABC Tower” in the stored content 1-1, “Paris” is extracted from “Tour of Paris” in 1-2, and “Las Vegas” from “Tour of Las Vegas” in 1-3. “Kanto” is extracted as a keyword from “Trip to Kanto” in the stored content 2-1, “Tokyo” is extracted from “Around Tokyo” in 2-2, and “Hiroshima” from “Excursion to Hiroshima” in 2-3. 
     The accompanying data estimation unit  108  then performs step S 215 . When a fraction of the keywords have a shared portion, the shared portion is used as new accompanying data. In this case, the location “Tokyo”, which is shared by the two stored contents 1-1 and 2-2, is used as accompanying data for the input content group. 
     Keywords that exhibit an inclusive relationship may be used as new accompanying data. In the present example, the location “Tokyo” is included in the broader concept “Kanto”. Therefore, “Tokyo”, “Kanto”, or both locations may be used as accompanying data for the input content group. Note that since relationships of geographical inclusiveness are used, the information processing device  10  needs to be provided with a dictionary for supplemental knowledge regarding locations. For example, dictionary data may be stored in the database  103  and managed by the content storage unit  101 . 
     On the other hand, the new accompanying data may be chosen as a keyword included exactly as is in accompanying data for the stored content for each input object. In such a case, the location “Tokyo”, which is included in both the stored content 1-1 for OBJ 1  and the stored content 2-2 for OBJ 2 , is used as the accompanying data for the input content group. 
     Furthermore, all of the keywords may simply be used as accompanying data. In this case, the locations “Tokyo”, “Paris”, “Las Vegas”, “Kanto”, and “Hiroshima” are all used as the accompanying data for the input content group. 
     Another method is to set the new accompanying data to be keywords whose locations are most concentrated over a geographical distance within a certain threshold. For example, if the threshold is 500 km or less, the two locations “Tokyo” and “Kanto” are within the threshold, whereas the other locations are not. Therefore, “Tokyo” and “Kanto” are used as the accompanying data for the input content group. In this case, “Tokyo” is a narrower concept than “Kanto”. Therefore, it is possible to use only one of the locations “Tokyo” and “Kanto” as the accompanying data for the input content group. Note that the threshold may be a predetermined value or a user-set value. 
     Furthermore, the number of times the same keyword, or equivalent keywords, occurs within a plurality of keywords may be acquired. When the acquired number of occurrences is at least a certain value, the keyword(s) may be used as accompanying data. Equivalent keywords are, for example, two keywords for which one is a broader concept and the other a narrower concept. 
     (2) Specific Example 2 
     Next, a specific example of using fixed accompanying data to estimate accompanying data regarding location is described. 
       FIG. 7  shows extracted stored content and corresponding fixed accompanying data for an input content group as a result of the information processing device  10  starting estimation of accompanying data related to location by performing processing from step S 5  through step S 25  in  FIG. 3 . 
     Note that operations from step S 5  through step S 25  are similar to the operations in Specific Example 1. A description thereof is thus omitted. In this state, accompanying data estimation (step S 30 ) begins. 
     The accompanying data estimation unit  108  then performs step S 200 . Since the obtained accompanying data is of one type, the accompanying data is used as is for estimation. 
     Next, the accompanying data estimation unit  108  performs step S 205 . The obtained accompanying data is fixed. Processing thus proceeds to step S 215 . 
     One method is to simply use all of the extracted accompanying data as the accompanying data for the input content group. In this case, in step S 215 , the accompanying data estimation unit  108  simply uses six GPS positions as accompanying data. Another method is to consider the six GPS positions as indicating a range rather than points. For example, GPS positions in a range of latitudes “from 34 degrees, 23 minutes, 739 seconds north to 48 degrees, 51 minutes, 30 seconds north” and longitudes “from 115 degrees, 10 minutes, 20 seconds west to 139 degrees, 45 minutes, 24 seconds east, traversing 0 degrees east” may be used as the accompanying data for the input content group. 
     Yet another method is to set the new accompanying data to be accompanying data indicating locations that are most concentrated over a geographical distance within a certain threshold. For example, if the threshold is 500 km or less, three GPS positions are used as the new accompanying data: “35 degrees, 39 minutes, 31 seconds north, 139 degrees, 44 minutes, 43 seconds east”, “35 degrees, 39 minutes, 20 seconds north, 139 degrees, 45 minutes, 24 seconds east”, and “35 degrees, 43 minutes, 48 seconds north, 139 degrees, 42 minutes, 40 seconds east”. Still another method is to consider these three GPS positions as indicating a range rather than points. In this case, a range of latitudes “from 35 degrees, 39 minutes, 20 seconds north to 35 degrees, 43 minutes, 48 seconds north” and longitudes “from 139 degrees, 42 minutes, 40 seconds east to 139 degrees, 45 minutes, 24 seconds east” is used as the accompanying data for the input content group. 
     Furthermore, a GPS position may be converted into a city name and used as the accompanying data for the input content group in the same way as described above for estimating accompanying data related to location from non-fixed accompanying data of stored contents. Note that in order to convert GPS positions to city names in this way, the information processing device  10  needs to be provided with a dictionary for supplemental knowledge regarding the relationship between locations and GPS positions. For example, dictionary data may be stored in the database  103  and managed by the content storage unit  101 . 
     (3) Specific Example 3 
     Next, a specific example of estimating accompanying data related to a time period from fixed accompanying data is described. 
       FIG. 8  shows extracted stored content and corresponding fixed accompanying data for an input content group as a result of the information processing device  10  starting estimation of accompanying data related to location by performing processing from step S 5  through step S 25  in  FIG. 3 . Note that operations from step S 5  through step S 25  are similar to the operations in Specific Example 1. A description thereof is thus omitted. In this state, accompanying data estimation (step S 30 ) begins. 
     The accompanying data estimation unit  108  then performs step S 200 . Since the obtained accompanying data is of one type, the accompanying data is used as is for estimation. 
     Next, the accompanying data estimation unit  108  performs step S 205 . The obtained accompanying data is fixed. Processing thus proceeds to step S 215 . 
     One method is to simply use all of the extracted accompanying data as the accompanying data for the input content group. In this case, in step S 215 , the accompanying data estimation unit  108  simply uses six dates and times as accompanying data. Another method is to consider the six dates and times as indicating a range rather than points. In this case, the range “from 1988/5/31, 13:37 to 2008/12/28 17:57” is used as the accompanying data for the input content group. 
     Yet another method is to set the new accompanying data to be a range in which time periods indicated by the accompanying data are most concentrated within a time range of a predetermined threshold. For example, if the threshold is ten years, then four dates and times are used as the new accompanying data: “1988/5/31, 13:37”, “1990/5/5, 14:45”, “1993/3/23, 15:07”, and “1998/8/18, 15:29”. Still another method is to consider these four dates and times as indicating a range rather than points. In this case, the range “from 1988/5/31, 13:37 to 1998/8/18 15:29” is used as the accompanying data for the input content group. 
     1.4 Modifications 
     (1) Modification to Estimation of Accompanying Data 
     In the above embodiment, one type of accompanying data is used for estimation, but estimation is not limited in this way. Accompanying data for the input content group may be estimated using a plurality of types of accompanying data. 
     With reference to the flowchart in  FIG. 9 , the following describes operations to estimate accompanying data for an input content group using a plurality of types of accompanying data. 
     The accompanying data estimation unit  108  first confirms the types of accompanying data, extracted in step S 25  of  FIG. 3 , of each stored content that includes a stored object similar to the input object. The accompanying data estimation unit  108  then classifies the types of accompanying data into types of accompanying data used to select accompanying data of the stored content and types of accompanying data used for estimation of the final accompanying data for the input content group (step S 300 ). Specifically, the accompanying data estimation unit  108  receives, via the receiving unit  110 , a designation by user operation of types of accompanying data to use for selection. The accompanying data estimation unit  108  then classifies the types of accompanying data used for estimation in accordance with the received designation. 
     The accompanying data estimation unit  108  determines whether one type of accompanying data, among the types of accompanying data extracted in step S 300  for selection of accompanying data of the stored content, is fixed (step S 305 ). 
     When determining that the one type is fixed (step S 305 : No), the accompanying data estimation unit  108  confirms the subject matter of the accompanying data of the one type and extracts accompanying data, or a fraction of accompanying data, that matches a selection criterion (step S 310 ). As described in the above embodiment, the subject matter of non-fixed type accompanying data is not predetermined and therefore may not match the criterion for selection of accompanying data. It is therefore necessary to perform step S 310  in order to confirm the subject matter of the accompanying data and extract accompanying data, or a fraction of accompanying data, that matches the selection criterion. The selection criterion refers to a criterion for selecting accompanying data of stored content that is estimated as being the most appropriate in step S 315 , described below. For example, the criterion may be as follows: to select accompanying data having a portion thereof that is shared among a plurality of accompanying data items, or fractions of accompanying data items; to select a plurality of accompanying data items that exhibit an inclusive relationship; to select the accompanying data that best matches a predetermined criterion among the extracted accompanying data or fraction of accompanying data; or to select the entire extracted accompanying data or fraction of accompanying data. Furthermore, a different criterion may be applied depending on the subject matter of the accompanying data, or on the subject matter of the accompanying data to be estimated. 
     After performing step S 310 , or when it has been determined that the one type of accompanying data is fixed (step S 305 : YES), the accompanying data estimation unit  108  selects the accompanying data of the stored content by combining, in accordance with the selection criterion, the fixed accompanying data determined in step S 305  or the accompanying data selected in step S 310  (step S 315 ). The method of combining accompanying data may, for example, be as follows: to select stored content having a portion thereof that is shared among a plurality of accompanying data items, or fractions of accompanying data items; to select stored content including accompanying data that best matches a predetermined criterion among the extracted accompanying data or fraction of accompanying data; or to select stored contents including the entire extracted accompanying data or fraction of accompanying data. Furthermore, a different method may be used depending on the subject matter of the accompanying data, or on the subject matter of the accompanying data to be estimated. 
     The accompanying data estimation unit  108  checks whether selection of accompanying data is complete (steps S 320 ). 
     Upon determining that selection of accompanying data is not complete (step S 320 : No), processing returns to step S 305 . The accompanying data estimation unit  108  then continues to select accompanying data of stored content. 
     Upon determining that selection is complete (step S 320 : Yes), the accompanying data estimation unit  108  determines whether the type of accompanying data used for estimation of accompanying data for the input content group is fixed (step S 325 ). 
     When determining that the type is not fixed (step S 325 : No), the accompanying data estimation unit  108  confirms the type of accompanying data used for estimation and extracts accompanying data, or a fraction of accompanying data, that matches the type of accompanying data for the input content group targeted for estimation (step S 330 ). This is for the same reason as described above: namely, the subject matter of non-fixed type accompanying data is not predetermined and therefore may not match the type of accompanying data to be estimated. 
     The accompanying data estimation unit  108  then generates the accompanying data estimated as having the highest probability of being the most appropriate for the input content group by selecting or combining the fixed type accompanying data of the stored contents and the accompanying data, or fraction of accompanying data, extracted in step S 330  (step S 335 ). The method of combining accompanying data may, for example, be as follows: to use accompanying data extracted as having a portion thereof that is shared among a plurality of accompanying data items, or fractions of accompanying data items; to use a plurality of accompanying data items that exhibit an inclusive relationship; to use accompanying data that best matches a predetermined criterion among the extracted accompanying data or fraction of accompanying data; or to use the entire extracted accompanying data or fraction of accompanying data. Furthermore, a different method may be used depending on the subject matter of the accompanying data of the stored content used for estimation, or on the subject matter of the accompanying data to be estimated. 
     Note that the standard for completion of selection in step S 320  differs depending on the nature of the selection. For example, if one type of accompanying data is only selected once, it should be determined whether any of the types of accompanying data extracted in step S 300  for selection of accompanying data of the stored content has not yet been used. On the other hand, if the same type of accompanying data is used multiple times for selection based on different selection criteria (for example, when using accompanying data that is a non-fixed keyword to select accompanying data of a stored content twice based on different criteria, such as location and time period), it should be determined whether selection of the accompanying data has been repeated a predetermined number of times. 
     (Specific Example) 
     A specific example of estimating accompanying data related to time periods from a plurality of types of fixed accompanying data of stored contents is now described. 
       FIG. 10  shows extracted stored content and two corresponding types of accompanying data (date/time and keyword) for an input content group as a result of the information processing device  10  starting estimation of accompanying data related to location by performing processing from step S 5  through step S 25  in  FIG. 3 . Note that operations from step S 5  through step S 25  are similar to the operations in Specific Examples 1-3. A description thereof is thus omitted. In this state, accompanying data estimation (step S 30 ) begins. 
     By performing step S 300 , the accompanying data estimation unit  108  for example classifies the date and time as the accompanying data (keyword) used when selecting the stored content for estimation of the final accompanying data. 
     The accompanying data estimation unit  108  then performs step S 305 . In this case, the keyword for selection is non-fixed data. Therefore, the accompanying data estimation unit  108  performs step S 310  next. For example, if stored content is selected based on the criterion “location”, the accompanying data estimation unit  108  selects only keywords related to location. Specifically, the accompanying data estimation unit  108  selects “Tokyo”, “Paris”, “Las Vegas”, “Tokyo”, “Kyoto”, and “Hiroshima”. 
     The accompanying data estimation unit  108  then performs step S 310 . Here, it is assumed that the method of selection is to select stored contents including accompanying data containing keywords with a shared portion. Specifically, the accompanying data estimation unit  108  selects the stored content 1-1 and the stored content 2-1, which both have the keyword “Tokyo”. 
     The accompanying data estimation unit  108  then performs step S 320 . Since the only accompanying data classified as being for selection is a keyword, the accompanying data estimation unit  108  proceeds to step S 325 . 
     The accompanying data estimation unit  108  then performs step S 325 . Since the accompanying data classified as being for estimation (date and time) is fixed, the accompanying data estimation unit  108  skips step S 330 . 
     The accompanying data estimation unit  108  then performs step S 335 . One method is to simply use all of the extracted accompanying data as the accompanying data for the input content group. In this case, the accompanying data estimation unit  108  uses two dates and times, “1964/5/3, 13:37” and “1964/5/5, 14:45” as the accompanying data for the input content group. Another method is to consider these two dates and times as indicating a range rather than points. In this case, the accompanying data estimation unit  108  uses the range “from 1964/5/3, 13:37 to 1964/5/5, 14:45” as the accompanying data for the input content group. Yet another method is to set the new accompanying data to be a range in which time periods indicated by the accompanying data are most concentrated within a predetermined time range. In this case as well, the range “from 1964/5/3, 13:37 to 1964/5/5, 14:45” is set as the accompanying data for the input content group. 
     In the example in  FIG. 10 , suppose for example that the Tokyo ABC tower was completed in 1956. If only the date and time of accompanying data type 1 is used to estimate the time period, then “1955/8/18, 15:29”, at which point the Tokyo ABC tower had not yet been completed, may end up being used as the accompanying data. It is thus clear that using a plurality of types of accompanying data is effective to narrow down the accompanying data. 
     (2) Modification 1 to Information Processing Device 
     In the above embodiment, the database  103  is provided within the information processing device  10 , but the database  103  is not limited in this way. The database  103  may be located external to the information processing device  10 , for example on a network. 
       FIG. 11  shows the internal structure of an information processing device  1000  used in a database  1203  on a network. Note that the same reference signs are provided for constituent elements having the same function as in the information processing device  10  of Embodiment 1. 
     The information processing device  1000  includes a content storage unit  1101 , a content group selection unit  102 , an object selection unit  104 , a content analysis unit  105 , a similar object specification unit  106 , an accompanying data extraction unit  107 , an accompanying data estimation unit  108 , an accompanying data writing unit  109 , and a receiving unit  110 . 
     An external device  1001  is connected over a network to the information processing device  1000  and has a database  1203 . 
     Over the network, the content storage unit  1101  stores, in the database  1203 , all of the digital contents managed by the information processing device  1000 , as well as the corresponding accompanying data and the feature amounts of objects included in the digital contents. The content storage unit  1101  manages the contents stored in the database  1203 . 
     The database  1203  of the external device  1001  stores digital content data managed by the content storage unit  1101  and accompanying data for the digital content data, as well as feature amounts of objects included in the digital content data. Note that the content stored by the database  1203  is similar to the database  103  in Embodiment 1, and therefore a description thereof is omitted. 
     Apart from having a different target of access, operations by the information processing device  1000  to estimate accompanying data for the input content group are the same as the operations in Embodiment 1. A description of operations is therefore omitted. 
     (3) Modification 2 to Information Processing Device 
     In the above embodiment, the database  103  is provided within the information processing device  10 , but the database  103  is not limited in this way. The information processing device may both use a database within the information processing device and also use a database over a network. 
       FIG. 12  shows the internal structure of an information processing device  1000   a  in the present modification. Note that the same reference signs are provided for constituent elements having the same function as in the information processing device  10  of Embodiment 1. 
     The information processing device  1000   a  includes a content storage unit  1101   a , a content group selection unit  102 , a database  1103   a , an object selection unit  104 , a content analysis unit  105 , a similar object specification unit  106 , an accompanying data extraction unit  107 , an accompanying data estimation unit  108 , an accompanying data writing unit  109 , and a receiving unit  110 . 
     An external device  1001   a  is connected over a network to the information processing device  1000   a  and has a database  1203   a.    
     The content storage unit  1101   a  stores, in the database  1103   a  and the database  1203   a , all of the digital contents managed by the information processing device  1000   a , as well as the corresponding accompanying data and the feature amounts of objects included in the digital contents. The content storage unit  1101   a  manages the contents stored in the database  1103   a  and the database  1203   a.    
     The database  1103   a  and the database  1203   a  store digital content data managed by the content storage unit  1101  a and accompanying data for the digital content data, as well as feature amounts of objects included in the digital content data. For example, the content storage unit  1101   a  may primarily use the database  1103   a , using the database  1203   a  as a backup of the database  1103   a.    
     Operations by the information processing device  1000   a  to estimate accompanying data for an input content group are the same as the operations in Embodiment 1, apart from use of the database  1103   a  and the database  1203   a  instead of the database  103 . A description of operations is therefore omitted. 
     Note that in the above explanation, one of the two databases is described as being used for backup (in the example above, the database  1203   a ), but usage is not limited in this way. 
     For example, the content storage unit  1101  a may primarily use the database  1203   a , using the database  1103   a  as a backup of the database  1203   a . Alternatively, the information processing device  1000   a  may divide up all of the managed digital contents for storage in the database  1103   a  and the database  1203   a  in accordance with the capacity of the databases. Furthermore, the information processing device  1000   a  may store the data for the digital contents in the database  1103   a  and store only the feature amounts and the accompanying data for the digital contents in the database  1203   a . Conversely, the information processing device  1000   a  may store the data for the digital contents in the database  1203   a  and store only the feature amounts and the accompanying data for the digital contents in the database  1103   a . In other words, any method that uses both the database  1103   a  and the database  1203   a  to store the digital contents is possible. 
     (4) Modification 3 to Information Processing Device 
     In the above embodiment, the database  103  is provided within the information processing device  10 , but the database  103  is not limited in this way. In addition to the database within the information processing device, the information processing device may also use a plurality of external databases. 
       FIG. 13  shows the internal structure of an information processing device  1000   b  in the present modification. Note that the same reference signs are provided for constituent elements having the same function as in the information processing device  10  of Embodiment 1. 
     The information processing device  1000   b  includes a content storage unit  1101   b , a content group selection unit  102 , a database  1103   b , an object selection unit  104 , a content analysis unit  105 , a similar object specification unit  106 , an accompanying data extraction unit  107 , an accompanying data estimation unit  108 , an accompanying data writing unit  109 , and a receiving unit  110 . 
     An external device  1001   a  is connected over a network to the information processing device  1000   a  and has a database  1203   b.    
     An external device  1002   b  is connected over a network to the information processing device  1000   a  and has n databases 1-n  1303   b.    
     The content storage unit  1101   b  stores, in the database  1103   b  and the database  1203   b , all of the digital contents managed by the information processing device  1000   b , as well as the corresponding accompanying data and the feature amounts of objects included in the digital contents. The content storage unit  1101   b  manages the contents stored in the database  1103   b  and the database  1203   b.    
     The database  1103   b  and the database  1203   b  store digital contents managed by the content storage unit  1101   b  and accompanying data for the digital contents, as well as feature amounts of objects included in the digital contents. 
     The databases 1-n  1303   b  are not managed by the information processing device  1000   b . In this context, and external database is, for example, a database managed by a digital content upload site. 
     The content storage unit  1101   b  uses the databases 1-n  1303   b  by, for example, having the similar object specification unit  106  extract stored objects that are similar to an input object from not only the database  1103   b  and the database  1203   b , but similarly from the digital contents managed by the databases 1-n  1303   b  as well. Specifically, when searching for similar objects, the similar object specification unit  106  acquires digital contents from the content storage unit  1101   b  for digital contents in not only the database  1103   b  and the database  1203   b , but also the databases 1-n  1303   b . The similar object specification unit  106  then analyzes the objects included in the acquired digital contents to calculate feature amounts and compares the feature amounts with the feature amounts of the input object. 
     The database  1103   b  and the database  1203   b  are used as in Modification 2. 
     Operations by the information processing device  1000   b  to estimate accompanying data for an input content group are the same as the operations in Embodiment 1, apart from use of the database  1103   b , the database  1203   b , and the databases 1-n  1303   b  instead of the database  103 . A description of operations is therefore omitted. 
     Note that the method of using the database  1103   b , the database  1303   b , and the databases 1-n  1303   b  is not limited to the above method. 
     For example, the information processing device  1000   b  may analyze the objects included in the digital contents in the databases 1-n  1303   b  in advance and store the feature amounts and accompanying data in the database  1103   b  or the database  1203   b  for management by the content storage unit  1101   b . The similar object specification unit  106  may then extract the managed feature amounts and accompanying data when searching for similar objects. 
     When searching for similar objects, the similar object specification unit  106  may also select a portion the digital contents managed by the information processing device  1000   b  in the database  1103   b , the database  1203   b , and the databases 1-n  1303   b . For example, an indication that a stored content is to be searched may be included in the accompanying data for the stored content, and the similar object specification unit  106  may compare only the feature amounts for stored contents whose accompanying data includes such an indication. Furthermore, if it is necessary to reduce the amount of calculation or increase processing speed, only stored contents including stored objects whose feature amounts have been analyzed in advance may be targeted for comparison of feature amounts, excluding from the comparison stored contents with unanalyzed feature amounts. 
     Depending on the method of generating the accompanying data of the stored content, degrees of priority may be assigned when determining similarity. For example, one method is to determine that accompanying data attached to digital contents directly by a digital still camera upon shooting, or accompanying data attached directly by a user, is highly reliable. During extraction of similar objects from stored content to which such accompanying data is attached, the allowable range of the difference in feature amounts may then be increased. 
     Furthermore, depending on the type of database, degrees of priority may be assigned when determining similarity. For example, the allowable range of the difference in feature amounts may be increased when extracting similar objects from the database  1103   b  or the database  1203   b , which are managed by the information processing device  1000   b . Conversely, the allowable range of the difference in feature amounts may be decreased when extracting similar objects from the databases 1-n  1303   b.    
     The allowable range of the difference in feature amounts may also be increased when extracting similar objects from a database, among the external databases 1-n  1303   b , which a user frequently uses. 
     Note that the above methods are only examples. The methods of using the digital content data on the external databases 1-n  1303   b  are not limited to the above methods. 
     Furthermore, the methods of using the database  1103   b  and the database  1203   b  are not limited to the methods described in Modification 2. For example, the database  1103   b  may store all of the content data, feature amounts, and accompanying data managed by the information processing device  1000   b , and the database  1203   b  may store the analysis results, feature amounts, and accompanying data of the digital contents on the external databases 1-n  1303   b . Alternatively, the database  1203   b  may store all of the content data, feature amounts, and accompanying data managed by the information processing device  1000   b , and the database  1103   b  may store the analysis results, feature amounts, and accompanying data of the digital contents on the external databases 1-n  1303   b.    
     The above methods of using the database  1103   b  and the database  1203   b  are only examples. Usage is in no way limited to these examples. 
     (5) Modification 4 to Information Processing Device 
     In the above embodiment, the contents targeted for estimation are located in the database  103 , but the location is not limited in this way. The information processing device may retrieve contents for estimation from an external source. 
       FIG. 14  shows the internal structure of an information processing device  1000   c  in the present modification. Note that the same reference signs are provided for constituent elements having the same function as in the information processing device  10  of Embodiment 1. 
     The information processing device  1000   c  includes a content storage unit  1101   c , an input receiving unit  1111   c , a database  1103   c , an object selection unit  104 , a content analysis unit  105 , a similar object specification unit  106 , an accompanying data extraction unit  107 , an accompanying data estimation unit  108 , an accompanying data writing unit  2109 , and a receiving unit  110 . 
     The content storage unit  1101   c  stores, in the database  1103   c , all of the digital contents managed by the information processing device  1000   c , as well as the corresponding accompanying data and the feature amounts of objects included in the digital contents. The content storage unit  1101   c  manages the contents stored in the database  1103   c.    
     The input receiving unit  1111   c  receives input, from an input device  1003   c , of one or more contents targeted for estimation. All of the contents targeted for estimation that are input are classified into one or more input content groups. The method of classifying into input content groups is similar to the operations of the content group selection unit  102  in Embodiment 1. Details are thus omitted. 
     The database  1103   c  stores digital contents managed by the content storage unit  1101   c , in particular the stored contents described in Embodiment 1. 
     The input device  1003   c  is for inputting contents targeted for estimation into the information processing device  1000   c . The input device  1003   c  is, for example, a digital still camera, video camera, Personal Computer (PC), Personal Video Recorder (PVR), or the like that store digital contents. The input device  1003   c  is not limited to these devices and may be any device storing digital contents. 
     Operations by the information processing device  1000   c  to estimate accompanying data for the input content group are the same as the operations in Embodiment 1, apart from the way in which contents targeted for estimation are acquired. A description of operations is therefore omitted. 
     1.5 Other Modifications 
     Other than the above embodiment and modifications, the present invention includes, for example, the following modifications. 
     (1) In the above embodiment, the stored content in the database  103  has been described as digital content data, feature amounts of the entire content, feature amounts of objects included in the content, and accompanying data, yet the stored content is not limited in this way. 
     The database  103  may be configured to store digital content data and associated accompanying data, without storing data for various types of feature amounts. 
     In this case, the similar object specification unit  106  calculates the feature amounts of the stored content and the feature amounts of the objects included in the stored contents. 
     (2) In the above embodiment, the contents targeted for estimation have been described as being managed without associated feature amounts, but the contents targeted for estimation are not limited in this way. 
     Feature amounts of an entire content targeted for estimation and feature amounts of an object included in the content targeted for estimation, i.e. an input object, may be stored in the database  103  in association with the content targeted for estimation. In this case, the content analysis unit  105  uses feature amounts associated in advance. 
     (3) In the above embodiment, the contents targeted for estimation are not associated with accompanying data in advance, but the contents targeted for estimation are not limited in this way. 
     Digital content associated with accompanying data may be a content targeted for estimation. In this case, accompanying data that has not yet been associated with the digital content is estimated. 
     For example, if only a date and time is associated with a content as accompanying data, the information processing device  10  may estimate a GPS position. 
     (4) In the above embodiments, the method of selecting contents targeted for estimation has been described as reception, by the receiving unit  110 , of an instruction via a remote control, mouse, keyboard, or the like, but the method of selection is not limited in this way. 
     If the information processing device  10  is provided with a touchscreen as the receiving unit  110 , the contents targeted for estimation may be selected by the user touching the screen with a finger or with a stylus such as a touch pen. 
     Furthermore, the information processing device  10  may be provided with a dedicated reception device as the receiving unit  110  for input of gestures or speech. 
     Note that the receiving unit  110  is in no way limited to the above examples. 
     Furthermore, the content group selection unit  102  may select a different selection criterion, not based on user input, for selection of contents targeted for estimation. Such a selection criterion may, for example, be that accompanying data, or a fraction thereof, is lacking. 
     The accompanying data of a digital content may also include selection information specifying that the digital content is to be selected. In this case, the content group selection unit  102  may refer to the selection information to select the contents targeted for estimation. 
     Additionally, contents targeted for estimation may be divided into groups and stored in advance. In this case, the content group selection unit  102  treats one such group as an input content group. 
     Note that the above methods of selection by the content group selection unit  102  are only examples, and selection is not limited to these methods. 
     (5) The method of classifying the contents targeted for estimation into input content groups is not limited to the method described in Embodiment 1. For example, when performing estimation for digital contents with accompanying data attached thereto, a plurality of contents targeted for estimation may be grouped based on indices such as date and time, event, or location. 
     (6) The type of accompanying data used for estimation has been described as being received as user input, but the type is not limited in this way. 
     The following sort of method may be used to select the type of accompanying data used for estimation. 
     Accompanying data that matches the type of accompanying data of the input content group that is targeted for estimation may be selected. Alternatively, a type of accompanying data that is lacking in the input content group, or a type of accompanying data as indicated in the accompanying data of the stored contents may be selected. 
     Note that the above methods of selection of accompanying data are only examples, and selection is not limited to these methods. 
     (7) The method of estimating accompanying data for the contents targeted for estimation is not limited to the methods in the above embodiment. 
     For example, a portion that is shared among a plurality of accompanying data items, or fractions of accompanying data items, may be extracted and used as accompanying data. 
     A plurality of accompanying data items that exhibit an inclusive relationship may be used as accompanying data, or the accompanying data that best matches a predetermined criterion among the extracted accompanying data or fraction may be used as accompanying data. 
     Additionally, the entire extracted accompanying data or fraction of accompanying data may be used. 
     Furthermore, a different estimation method may be used depending on the subject matter of the accompanying data of the stored content used for estimation, or on the subject matter of the accompanying data to be estimated. 
     (8) When using a plurality of types of accompanying data, the method for classifying types for estimation and types for selection is not limited to the above methods. 
     For example, if a type of accompanying data matches the type of accompanying data of the input content group that includes the contents targeted for estimation, that type of accompanying data may be classified as a type for estimation, and the remaining types of accompanying data may be classified for selection. 
     The types of accompanying data used for estimation or selection may also be classified in accordance with an indication in the accompanying data of types of accompanying data of stored contents. 
     Note that the above methods of classification of accompanying data are only examples, and selection is not limited to these methods. 
     (9) In the above embodiment, the object selection unit  104  receives a user indication of objects included in the contents targeted for estimation, but the object selection unit  104  is not limited in this way. 
     The object selection unit  104  may recognize objects within a digital content using general object recognition technology in order to select an object appropriate for estimation of accompanying data. 
     Furthermore, if an object to be selected is indicated in the accompanying data of a digital content included in the input content group, the object selection unit  104  may select the object indicated in the accompanying data as the input object. 
     Note that the method of selecting an object is not limited to these methods. 
     (10) Specification of similar objects by the similar object specification unit  106  is not limited to the above methods. 
     For example, when extracting similar objects to the input objects, the similar object specification unit  106  does not always have to compare the feature amounts of the input object with every stored object in every stored content in the database  103 . In this case, the accompanying data of a stored content may include an indication that comparison of feature amounts is to be made for the stored content. The similar object specification unit  106  may then extract and compare feature amounts only for stored contents that include this indication. 
     Alternatively, when similar objects to the input objects are extracted, weighting in accordance with the method of generating accompanying data of the stored content may be used in the comparison between feature amounts of stored objects and feature amounts of input objects. For example, accompanying data attached to digital contents directly by a digital still camera upon shooting, or accompanying data attached directly by a user, can be considered more reliable. The allowable range of the difference in feature amounts may therefore be increased when determining whether a stored object included in a stored content to which highly reliable accompanying data is attached is a similar object. The target of comparison of feature amounts may be restricted to stored objects included in stored contents to which highly reliable accompanying data is attached. Furthermore, if it is necessary to reduce the amount of calculation or increase processing speed, only stored contents including stored objects whose feature amounts have been analyzed in advance may be targeted for comparison, excluding from comparison stored contents with unanalyzed feature amounts. 
     Note that the method of selecting a stored object for comparison of feature amounts is not limited to these methods. 
     (11) If the similar object specification unit  106  performs weighting when comparing feature amounts, the accompanying data writing unit  109  may further record an indication that the accompanying data was derived using weighting. 
     (12) In the above embodiment, when a similar object is extracted for an input object, a weight may be added to the extracted similar object (a matching ratio with the input object, i.e. a degree of reliability of the matching), and the accompanying data for the input content group may be estimated based on the attached weight. For example, it is possible to use accompanying data for similar objects with the highest weight among weighted similar objects. 
     The weight of similar objects is not limited to the matching ratio with the input object, but may instead be based on the number of the same objects included in the contents targeted for estimation. For example, the weight may be a ratio of the number of same objects to the total number of objects. 
     (13) In the above embodiment, the input content may differ in type from the digital content selected as the stored content. For example, a video may be selected as the input content, whereas a still image may be selected as the stored content. Conversely, a still image may be selected as the input content, whereas a video may be selected as the stored content. 
     Furthermore, a plurality of types of digital content may be selected as the input content or the stored content. For example, both a still image and a video may be selected as the input content. 
     1.7 Summary 
     As described above, the information processing device in the embodiment and the modifications can estimate accompanying data for all of the contents targeted for estimation included in the input content groups from accompanying data associated with stored contents that include similar objects. 
     2. Embodiment 2 
     The following describes the internal structure of an information processing device in Embodiment 2 of the present invention. 
     2.1 Structure 
     In Embodiment 1, the accompanying data for the contents targeted for estimation is estimated based on the accompanying data associated with stored content. In Embodiment 2, on the other hand, the accompanying data for the contents targeted for estimation is estimated based on dictionary data prepared in advance. 
     The following describes the structure of the information processing device  2000  in the present embodiment. Note that the same reference signs are provided for constituent elements having the same function as in the information processing device  10  of Embodiment 1. 
     As shown in  FIG. 15 , the information processing device  2000  includes a content storage unit  2101 , a content group selection unit  102 , a database  2103 , an object selection unit  104 , a content analysis unit  105 , a dictionary object specification unit  2106 , an accompanying data extraction unit  2107 , an accompanying data estimation unit  2108 , an accompanying data writing unit  109 , and a receiving unit  110 . 
     (1) Database  2103   
     As shown in  FIG. 16 , the database  2103  includes a content storage region  2200 , a general dictionary data storage region  2201 , and a user dictionary data storage region  2202 . 
     The content storage region  2200  stores stored contents and contents targeted for estimation, as in Embodiment 1. Since the stored contents and the contents targeted for estimation have been described in Embodiment 1, a description thereof is omitted here. 
     The general dictionary data storage region  2201  and the user dictionary data storage region  2202  are regions storing dictionary data. 
     Dictionary data refers to data that links a dictionary object with feature amounts of the dictionary object and with accompanying data estimated from the dictionary object. 
     The dictionary data is divided into general dictionary data and user dictionary data. General dictionary data is a general collection of links between objects and accompanying data, whereas user dictionary data is a user-defined collection of links between objects and accompanying data. The general dictionary data is stored in the general dictionary data storage region  2201 , whereas the user dictionary data is stored in the user dictionary data storage region  2202 . 
     The general dictionary data storage region  2201  stores dictionary data  2210 ,  2211 , . . . related to location, time period, and the like. For example, the dictionary data  2210  is related to location, and the dictionary data  2211  is related to time period. 
     The user dictionary data storage region  2202  stores dictionary data  2230 ,  2231 , . . . related to location, time period, and the like. For example, the dictionary data  2230  is related to location, and the dictionary data  2231  is related to time period. 
     The dictionary data includes a plurality of combinations of a dictionary object, the feature amounts for the dictionary object, and one or more accompanying data items (hereinafter, “object information”). For example, the dictionary data  2210  includes object information  2220 , . . . ,  2221 . The object information  2220  is composed of a dictionary object (object A), the feature amounts for the dictionary object, and one or more accompanying data items (in this case, accompanying data A 1 , . . . , Aa). Each accompanying data item is also used to identify the corresponding object. 
     The following describes a specific example of the content of dictionary data. If objects in a photograph taken by a digital still camera include “Tokyo ABC Tower”, a keyword related to location, such as “Tokyo” or “ABC town” can easily be estimated as a keyword for the location where the photograph was produced. In this case, the dictionary object in the general dictionary is set to “Tokyo ABC Tower”, and the accompanying data for the dictionary object is set to “Tokyo”, “ABC town”, or the like. In this way, dictionary data indicating the location of an object, thereby limiting the location in which the object exists, is included in the dictionary data  2210  as dictionary data related to location. 
     On the other hand, if objects in a photograph taken by a digital still camera include a “car”, the time frame from the year in which sales of the “car” begin until approximately ten years after manufacturing of the “car” was discontinued can be offered as a likely candidate for dictionary data related to the time period for production of the photograph. This is because a new model of a “car” typically comes out every few years, the manufacturing and sale of a certain type of “car” is limited, and the average life expectancy of a “car” is approximately ten years. It is plausible, however, for a “car” to be in use for more than ten years. Therefore, accompanying data related to the time period of a dictionary object may be listed in conjunction with accompanying data related to a probability of existence for each time period. An object whose time frame of existence is limited may in this way be treated as dictionary data indicating a time period. 
     If objects in a photograph taken by a digital still camera include a “car the user owned from 1990 to 1995”, accompanying data related to a time period, such as “from 1990 to 1995”, can easily be estimated as a time period in which the photograph was produced. In this sort of case, the dictionary object in the user dictionary data is set to “car the user owned from 1990 to 1995”, and the accompanying data for the dictionary object is set to “from 1990 to 1995”. In this way, dictionary data can indicate the time frame or location of an object, thereby limiting the time period or location in which the object exists, among objects related to a user. 
     (2) Content Storage Unit  2101   
     The content storage unit  2101  stores and manages the stored contents, the contents targeted for estimation, and the dictionary data in the database  2103 . 
     (3) Dictionary Object Specification Unit  2106   
     The dictionary object specification unit  2106  extracts, from among objects in the dictionary data managed by the content storage unit  2101 , one or more objects (dictionary objects) in the dictionary data that are similar to each input object. 
     (4) Accompanying Data Extraction Unit  2107   
     For each of the dictionary objects extracted from the dictionary data for each input object, the accompanying data extraction unit  2107  extracts all of the accompanying data associated with the dictionary object. 
     (5) Accompanying Data Estimation Unit  2108   
     The accompanying data estimation unit  2108  uses the extracted accompanying data to estimate accompanying data for each input content group. 
     The method of estimation may be similar to the method in Embodiment 1 when accompanying data related to probability of existence is not indicated in the accompanying data of the dictionary object. On the other hand, when such probability of existence is indicated, the probability of existence indicated for each accompanying data item is added together between accompanying data items, and the accompanying data item with the largest resulting sum of probabilities of existence is used as the accompanying data. 
     2.2 Operations 
     (1) Outline of Operations 
     The following describes an outline of operations by the information processing device  2000  with reference to the flowchart in  FIG. 17 . 
     The content group selection unit  102  classifies one or more contents targeted for estimation into one or more input content groups based on user operation (step S 400 ). 
     The object selection unit  104  selects one or more input objects for each content targeted for estimation included in each input content group (step S 405 ). Specifically, as in Embodiment 1, the object selection unit  104  receives a designation, provided by user operation, of a region that includes an object in the content targeted for estimation. Upon receiving the designation, the object selection unit  104  selects the designated region as an input object. 
     The content analysis unit  105  calculates feature amounts for each of the one or more input objects selected by the object selection unit  104  (step S 410 ). 
     For each of the input objects extracted in step S 405 , the dictionary object specification unit  2106  extracts one or more dictionary objects that are similar to the input object from among one or more objects included in each piece of the dictionary data (step S 415 ). The determination of similarity between an object and an input object is similar to Embodiment 1. A description of the determination is thus omitted here. 
     For each of the one or more dictionary objects extracted in step S 415 , the accompanying data extraction unit  2107  extracts the accompanying data of the dictionary object (step S 420 ). 
     The accompanying data estimation unit  2108  uses the accompanying data extracted in step S 420  to estimate accompanying data for each input content group (step S 425 ). 
     For each input content group, the accompanying data writing unit  109  records the accompanying data estimated in step S 425  for all of the contents targeted for estimation included in the input content group (step S 430 ). 
     (2) Classification 
     The classification shown in step S 400  of  FIG. 17  can be achieved by similar operations as in the flowchart of  FIG. 4 . A description of the classification is thus omitted here. 
     (3) Estimation of Accompanying Data 
     The following describes the estimation of accompanying data in step S 425  of  FIG. 17  in detail with reference to the flowchart in  FIG. 18 . Note that this estimation process is performed for each content targeted for estimation. 
     Via the receiving unit  110 , the accompanying data estimation unit  2108  receives a user indication of a type of accompanying data to use for estimation from among the accompanying data of dictionary objects extracted in step S 420  of  FIG. 17  (step S 500 ). 
     The accompanying data estimation unit  2108  determines whether the received type of accompanying data is fixed (step S 505 ). Specifically, as in Embodiment 1, the accompanying data estimation unit  2108  determines whether the received type of accompanying data is fixed based on the storage region, for a stored content, of accompanying data corresponding to the received type of accompanying data. 
     If the type of accompanying data is not fixed, i.e. if the type of accompanying data is determined to be non-fixed (step S 505 : No), the accompanying data estimation unit  2108  uses one or more dictionary objects for each input content group to extract accompanying data, or a fraction of accompanying data, that matches the received type of accompanying data (step S 510 ). 
     After performing step S 510 , or when it has been determined that the received type of accompanying data is fixed (step S 505 : YES), the accompanying data estimation unit  2108  selects or combines the fixed accompanying data, or the accompanying data or a fraction thereof extracted in step S 510 , in order to generate the accompanying data estimated as having the highest probability of being the most appropriate accompanying data for the input content group (step S 515 ). 
     2.3 Modifications 
     Other than the above embodiment, the present invention includes, for example, the following modifications. 
     (1) In the above embodiment, when extracting dictionary objects similar to an input object, the dictionary object specification unit  2106  compares the feature amounts of the input object with the feature amounts of each object in every piece of dictionary data, but comparison is not limited in this way. 
     It is not necessary to compare every object with the feature amounts of the input object. For example, the accompanying data of an object may include an indication that comparison of feature amounts is to be made for the object. Only such objects may then be extracted for comparison of feature amounts. 
     Alternatively, when similar dictionary objects to the input objects are extracted, weighting in accordance with the type of dictionary data may be used in the comparison between feature amounts of dictionary objects and feature amounts of input objects. For example, if the user dictionary data is to be prioritized, the allowable range of the difference in feature amounts may be increased when determining whether an object included in the user dictionary data is a dictionary object similar to an input object. Alternatively, the target of comparison of feature amounts may be limited to objects included in the user dictionary data to be prioritized. Another option is to weight the similarity of a dictionary object in accordance with the degree of reliability of the creator of the dictionary. Note that the method of selecting an object for comparison of feature amounts is not limited to these methods. 
     (2) In the above embodiment, when probability of existence is indicated, the accompanying data estimation unit  2108  adds the probability of existence indicated for each accompanying data item together and uses the accompanying data item with the largest resulting sum of probabilities of existence as the accompanying data. However, operations are not limited in this way. 
     For example, the accompanying data estimation unit  2108  may use all accompanying data whose sum of probabilities of existence is higher than a certain threshold. 
     Furthermore, different conditions may be set depending on the subject matter of the accompanying data used for estimation, or on the subject matter of the accompanying data to be estimated. 
     (3) The information processing device shown in Embodiment 2 may perform estimation of accompanying data using a database on a network as shown in  FIGS. 11 and 12 , estimation of accompanying data using the databases 1-n shown in  FIG. 13 , or estimation of accompanying data using an input device as shown in  FIG. 14 . 
     In this case, operations differ only in that accompanying data of dictionary objects is used, and in that probability of existence is used as the method of estimating accompanying data, as shown in the example in  19 . The concept behind operations, however, does not differ, and thus a description is omitted here. 
     Furthermore, estimation using accompanying data of similar objects, as in Embodiment 1, and estimation using accompanying data of dictionary objects, as in the present embodiment, may be combined. In this case, the method of estimating accompanying data using similar objects and the method of estimating accompanying data using dictionary objects may be performed either in parallel or sequentially in order to select or estimate accompanying data. 
     2.4 Specific Example 
     Next, a specific example of using dictionary data to estimate accompanying data regarding time period is described. 
       FIG. 19  shows dictionary objects and corresponding fixed accompanying data (time period and probability of existence) obtained as a result of the information processing device  2000  starting estimation of accompanying data related to time period by performing processing from step S 400  through step S 420  in  FIG. 17 . 
     The following is a detailed description of the steps through which the accompanying data for the dictionary objects shown in  FIG. 19  is obtained. In step S 400 , the content group selection unit  102  selects a photograph that includes a “user” (contents targeted for estimation C 200 ) and a photograph that includes a “car” (contents targeted for estimation C 201 ) as an input content group. 
     Next, in step S 405 , the object selection unit  104  selection a region that includes the “user” in the contents targeted for estimation C 200  (hereinafter, OBJ 1   a ) and a region that includes the “car” (hereinafter, OBJ 2   a ) as input objects. 
     Next, in step S 410 , the content analysis unit  105  calculates the feature amounts of the two input objects (OBJ 1   a , OBJ 2   a ). 
     In step S 415 , the dictionary object specification unit  2106  extracts dictionary objects having feature amounts that are close to the feature amounts of either of the two input objects. Specifically, for OBJ 1   a , a face recognition function is used to recognize that the object is the “user”, and relationships between faces and time periods in the user dictionary are referred to in order to extract time periods, along with probabilities, for a face resembling the “user”. For OBJ 2   a , two “cars” that are similar are extracted and displayed along with time periods and probabilities of existence. In this state, accompanying data estimation (step S 425 ) begins. 
     Next, the accompanying data estimation unit  2108  performs step S 500 . Since the obtained accompanying data is of one type, the accompanying data is used as is for estimation. 
     Next, the accompanying data estimation unit  2108  performs step S 505 . Since the accompanying data used for estimation is fixed, the accompanying data estimation unit  2108  performs step S 515 . If, for example, the accompanying data item with the largest sum of probabilities of existence is used as the accompanying data, then the accompanying data for 2001 is used, since the sum of probabilities of existence is the highest for 2001, namely 55%. 
     If, on the other hand, timer periods with a sum of probabilities of existence of at least 50% are used as the accompanying data, then 2001 and 2009-2010 are used as the accompanying data, since the sum of probabilities of existence is at least 50% for each of these time periods. Note that the threshold (in this case, 50%) may be a predetermined value or a user-set value. 
     The time periods may also be used as a range. In this case, the range from 2001 to 2010 is used as the accompanying data. Furthermore, if the dictionary object 2-1 and the dictionary object 2-2 are different objects, and the OBJ 20  is assumed to match only one of these objects, then instead of simply summing the probabilities of existence in the accompanying data items of the three types of dictionary objects, the sum of the probabilities of existence in the accompanying data items of dictionary object 1-1 and of dictionary object 2-1 may be calculated separately from the sum of the probabilities of existence in the accompanying data items of dictionary object 1-1 and of dictionary object 2-2. The time period 2001, which has the highest sum of probabilities of existence, may then be used as the accompanying data. 
     Furthermore, it is possible to estimate accompanying data using a plurality of types of dictionary data. In this case, processing is similar to the processing in  FIG. 9  of Embodiment 1, apart from use of accompanying data of dictionary objects as the accompanying data for selection and estimation, and use of probability of existence as the method combining accompanying data during estimation, as shown in the example in  FIG. 19 . Therefore, a description of such processing is omitted. 
     2.5 Summary 
     As described above, the information processing device  2000  in the present embodiment can estimate accompanying data for input content groups using dictionary data. 
     3. Embodiment 3 
     The following describes the internal structure of an information processing device in Embodiment 3 of the present invention. 
     3.1 Structure 
     Accompanying data for contents targeted for estimation is estimated based on accompanying data associated with stored contents in Embodiment 1 and based on dictionary data in Embodiment 2. In Embodiment 3, on the other hand, accompanying data for contents targeted for estimation is estimated based on data for age estimation. 
     The following describes the structure of the information processing device  3000  in the present embodiment. Note that the same reference signs are provided for constituent elements having the same function as in the information processing device  10  of Embodiment 1. 
     The information processing device  3000  includes a content storage unit  3101 , a content group selection unit  102 , a database  3103 , an object selection unit  104 , a content analysis unit  105 , an age estimation object specification unit  3106 , an accompanying data extraction unit  3107 , an accompanying data estimation unit  3108 , an accompanying data writing unit  109 , and a receiving unit  110 . 
     (1) Database  3103   
     As shown in  FIG. 21 , the database  3103  includes a content storage region  3200  and a user data storage region  3203 . 
     The content storage region  3200  stores stored contents and contents targeted for estimation, as in Embodiment 1. Since the stored contents and the contents targeted for estimation have been described in Embodiment 1, a description thereof is omitted here. 
     The general dictionary data storage region  3201  and the user dictionary data storage region  3202  respectively store general dictionary data and user dictionary data, as in Embodiment 2. Note that the general dictionary data and the user dictionary data have already been described in Embodiment 2. Further description is thus omitted here. 
     As shown in  FIG. 21 , the user data storage region  3203  stores date of birth data  3210  and age estimation data  3211 . 
     The date of birth data  3210  associates one or more feature amounts and a date of birth with each of a plurality of users (in this example, user a, user b, . . . , user n). The date of birth data  3210  is used to specify a subject included in a content and the age of the subject. 
     The age estimation data  3211  associates accompanying data (feature amounts, corresponding user(s), and estimated age of user) with each object or event for estimating the age of an object included in a content targeted for estimation. Hereinafter, an object or event for estimating age is referred to as an age estimation object. Each accompanying data item is also used to identify the corresponding object or event. 
     Objects or events by which age can be estimated include, for example, “school uniform” or “smoking”. School uniforms can be used to estimate age since typically a user wearing a school uniform would be junior high or high school age. Smoking can also be used to estimate the age of a user, since minors are not allowed to smoke in Japan. Objects or events may also be based on a user&#39;s particular circumstances. Note that age estimation objects are not limited to these examples. Any sort of object or event that allows for estimation of a user&#39;s age is possible. 
     (2) Content Storage Unit  3101   
     The content storage unit  3101  stores and manages the stored contents, the contents targeted for estimation, the general dictionary data, the user dictionary data, the date of birth data, and the age estimation data in the database  3103 . 
     (3) Age Estimation Object Specification Unit  3106   
     For each input object, the age estimation object specification unit  3106  uses the date of birth data  3210  to specify a user similar to the input object, while also extracting one or more age estimation objects similar to the input object from among age estimation objects included in the age estimation data  3211 . 
     Specifically, the age estimation object specification unit  3106  specifies a user similar to the input object based on feature amounts of the input object calculated by the content analysis unit  105  and one or more feature amounts of users included in the date of birth data  3210 . 
     The age estimation object specification unit  3106  extracts one or more age estimation objects that correspond to the accompanying data that includes the specified user and that are similar to the input object. Specifically, the age estimation object specification unit  3106  first acquires one or more age estimation objects that are similar to the input object based on feature amounts of the input object calculated by the content analysis unit  105  and feature amounts included in the age estimation data. The age estimation object specification unit  3106  then acquires, from among the one or more extracted age estimation objects, all of the age estimation objects whose accompanying data includes the user specified as being similar to the input object. 
     Stating that an age estimation object is similar to an input object means that the feature amounts of the input object and the feature amounts of the age estimation object either match or differ within an allowable range. A predetermined value or a user-set value may be used as the allowable range. 
     The concept of including a particular user in accompanying data refers to information on users in the accompanying data (user information) matching the specified user, or to the user information explicitly including the specified user. 
     (4) Accompanying Data Extraction Unit  3107   
     The accompanying data extraction unit  3107  extracts an estimated age corresponding to the acquired age estimation object. 
     (5) Accompanying Data Estimation Unit  3108   
     The accompanying data estimation unit  3108  uses the date of birth corresponding to the user specified by the age estimation object specification unit  3106  and the accompanying data of the age estimation object extracted by the accompanying data extraction unit  3107  in order to estimate the time period of the input content group. 
     3.2 Operations 
     (1) Outline of Operations 
     The following describes an outline of operations by the information processing device  3000  with reference to the flowchart in  FIG. 22 . 
     The content group selection unit  102  classifies one or more contents targeted for estimation into one or more input content groups based on user operation (step S 600 ). 
     The object selection unit  104  selects one or more input objects for each content targeted for estimation included in each input content group (step S 605 ). Specifically, as in Embodiment 1, the object selection unit  104  receives a designation, provided by user operation, of a region that includes an object in the content targeted for estimation. Upon receiving the designation, the object selection unit  104  selects the designated region as an input object. 
     The content analysis unit  105  calculates feature amounts for each of the one or more input objects selected by the object selection unit  104  (step S 610 ). 
     For each of the input objects extracted in step S 605 , the age estimation object specification unit  3106  performs age estimation object acquisition processing in order to specify a user similar to the input object and to acquire one or more age estimation object (step S 615 ). 
     For each of the one or more age estimation objects extracted in step S 615 , the accompanying data extraction unit  3107  extracts an estimated age for the age estimation object (step S 620 ). 
     The accompanying data estimation unit  2108  estimates the time period for each input content group (step S 625 ). 
     For each input content group, the accompanying data writing unit  109  records the time period estimated in step S 625  as the accompanying data for all of the contents targeted for estimation included in the input content group (step S 630 ). 
     (2) Classification 
     The classification shown in step S 600  of  FIG. 22  can be achieved by similar operations as in the flowchart of  FIG. 4 . A description of the classification is thus omitted here. 
     (3) Age Estimation Object Extraction 
     The following describes operations for age estimation object extraction shown in step S 615  of  FIG. 22  with reference to the flowchart in  FIG. 23 . 
     From among the input objects extracted in step S 605 , the age estimation object specification unit  3106  selects an input object in which to search for age estimation objects (step S 700 ). 
     The age estimation object specification unit  3106  determines whether a user has been specified as similar to the selected input object based on the feature amounts of the input object and the feature amounts of user a, . . . , user n included in the date of birth data (step S 705 ). 
     If a user has been specified (step S 705 : Yes), the age estimation object specification unit  3106  acquires the date of birth associated with the specified user (step S 710 ). Furthermore, the age estimation object specification unit  3106  extracts one or more age estimation objects similar to the selected input object (step S 715 ). 
     The age estimation object specification unit  3106  then determines whether extraction of age estimation objects is complete for all of the input objects extracted in step S 605  (step S 720 ). 
     If extraction is determined not to be complete (step S 720 : No), processing returns to step S 700 . If extraction is determined to be complete (step S 720 : Yes), processing terminates. 
     Furthermore, if it is determined that no user has been specified as similar to the selected input object (step S 705 : No), processing proceeds to step S 720 . 
     (4) Time Period Estimation 
     The following describes the estimation of accompanying data in step S 625  of  FIG. 22  in detail with reference to the flowchart in  FIG. 24 . Note that this estimation process is performed for each content targeted for estimation. 
     The accompanying data estimation unit  3108  selects one age estimation object for age estimation of one content targeted for estimation (step S 800 ). 
     The accompanying data estimation unit  3108  estimates the time period of the content targeted for estimation based on the estimated age of the age estimation object (step S 805 ). The following describes a specific example. Note that the content targeted for estimation is assumed to include a “user a wearing a school uniform” as an input object. In this case, the accompanying data estimation unit  3108  selects “school uniform” as the age estimation object in step S 800 . Based on the estimated age for this age estimation object, 12-18 years old, and the date of birth of user a (for example, Jun. 13, 1976), the time period for the input object is estimated to be “1988-1994”. 
     The accompanying data estimation unit  3108  determines whether the time period has been estimated for every input object included in the content targeted for estimation (step S 810 ). 
     If the time period has not been estimated for every input object (step S 810 ), processing returns to step S 800 . 
     If the time period has been estimated for every input object (step S 810 : Yes), the accompanying data estimation unit  3108  combines the estimated time periods to yield the most appropriate time period for the input content group as the accompanying data (step S 815 ). 
     3.3 Modifications 
     Other than the above embodiment, the present invention includes, for example, the following modifications. 
     (1) In the above embodiment, when extracting age estimation objects similar to an input object, the age estimation object specification unit  3106  compares the feature amounts of the input object with every age estimation object included in the age estimation data  3211 . Comparison is not, however, limited in this way. 
     For example, the age estimation object specification unit  3106  may perform the comparison only on age estimation objects that include an indication, in the accompanying data, that the feature amounts are to be compared. 
     The age estimation object specification unit  3106  may acquire, age estimation objects that are similar to an input object from among all of the age estimation objects whose accompanying data includes a specified user. 
     Note that the method of selecting age estimation objects for the comparison of feature amounts is not limited to these methods. 
     (2) The function to estimate accompanying data using dictionary data as described in Embodiment 2 may be added to the above embodiment. 
     Furthermore, the estimation functions of Embodiments 1 and 2 and of the present embodiment may be combined for a comprehensive estimation of the most appropriate accompanying data for an input content group based on stored objects, dictionary objects, and age estimation objects. 
     (3) In the above embodiment, the information processing device  3000  estimates a time period in step S 805  of  FIG. 24 , but the information processing device  3000  is not limited in this way. 
     For example, the information processing device  3000  may calculate time periods for combinations of users and age estimation objects in advance based on the date of birth data and the age estimation data and store the calculated time periods in the database  3103  in association with the combinations of users and age estimation objects. 
     When time periods are calculated in advance as described above, information on the time period associated with each combination of a user and an age estimation object may be stored in the database  3103  as user dictionary data, as described in Embodiment 2. The age estimation object may then be used as a portion of the dictionary object. 
     (4) In the above embodiment, the method of estimating the time period uses the date of birth data  3210  and the age estimation data  3211 , but the method is not limited in this way. 
     For example, the date of birth of each user from user a to user n may be stored in the database  3103  in association with feature amounts for the user&#39;s face at different ages. In this case, the age estimation object specification unit  3106  can acquire an age estimation object (here, a user&#39;s face) similar to an input object by comparing the feature amounts of the input object with the feature amounts of a user at the different ages. 
     Furthermore, instead of storing feature amounts for the user&#39;s face at different ages, feature amounts for the user&#39;s face during different time frames, such as the user&#39;s face at age 10-19, at age 20-29, etc. may be stored in association with the date of birth. 
     3.4 Summary 
     As described above, the information processing device  3000  of the present embodiment can estimate accompanying data (in this example, a time period) for input content groups. 
     4. Modifications 
     The present invention has been described through the above embodiments, but the present invention is in no way limited to these embodiments. For example, the following modifications are possible. 
     (1) A program listing steps for the methods described in the above embodiments may be stored in a memory. A Central Processing Unit (CPU) or the like may read the programs from memory and execute the read programs in order to achieve the above methods. 
     The program listing the steps for the above methods may also be stored on a recording medium and distributed. 
     (2) Each structure in the above embodiments may be implemented as a Large Scale Integration (LSI), which is a type of integrated circuit. These structures may respectively be made into discrete chips, or part or all of the structures may be made into one chip. Although referred to here as an LSI, depending on the degree of integration, the terms Integrated Circuit (IC), system LSI, super LSI, or ultra LSI are also used. Furthermore, the method of integration is not limited to LSI. Integration may be achieved via a dedicated circuit or a general-purpose processor. Alternatively, a Field Programmable Gate Array (FPGA), which is an LSI that can be programmed after manufacture, or a reconfigurable processor, which is an LSI whose connections between internal circuit cells and settings for each circuit cell can be reconfigured, may be used. Furthermore, calculation by these functional blocks may be performed using, for example, a Digital Signal Processor (DSP) or CPU. These processing steps may be recorded on a recording medium as a program and executed in order to perform the above processing. 
     Additionally, if technology for integrated circuits that replaces LSIs emerges, owing to advances in semiconductor technology or to another derivative technology, the integration of functional blocks may naturally be accomplished using such technology. The application of biotechnology or the like is possible. 
     (3) The above embodiments and modifications may be combined with one another. 
     5. Supplementary Explanation 
     (1) An aspect of the present invention is an information processing device comprising: a first acquisition unit operable to acquire a plurality of contents; a second acquisition unit operable to acquire one or more characteristics of each acquired content and to search among a plurality of reference data items in order to acquire one or more reference data items that each include any of the one or more characteristics, each reference data item having attached thereto at least one attribute used for identifying the reference data item, the reference data items being data other than the plurality of contents; a specification unit operable to specify, among the attributes attached to the acquired one or more reference data items, an attribute that also pertains to the plurality of contents, the specified attribute being used for identifying each of the plurality of contents; and an attachment unit operable to attach the specified attribute to each of the plurality of contents. 
     With the above structure, the information processing device uses the attributes attached to the reference data items to specify a shared attribute that is to be used to identify the plurality of contents and attaches the specified attribute to each of the plurality of contents. Therefore, the information processing device can attach a shared attribute to a content group composed of a plurality of contents. By attaching the shared attribute to each of the plurality of contents, the information processing device can, for example, search the content group using the attached attribute as a keyword for searching. 
     (2) The plurality of contents may be shot images and each include at least one object, each reference data item may include at least one object identified by the attribute attached to the reference data item, the second acquisition unit may acquire, as the one or more characteristics of each acquired content, one or more objects included in each of the contents and acquire, for each acquired object, one or more similar objects that are similar to the acquired object by searching among the objects included by the plurality of reference data items, and the specification unit may acquire, for each of the one or more similar objects, the attribute that identifies the object and is attached to the reference data item that includes the object. 
     With this structure, the information processing device can use, as the characteristics, the attributes attached to objects that are the same as or similar to the acquired object. This allows for easy specification of the shared attribute that is to be used to identify the plurality of contents. 
     (3) Each reference data item may be an image that includes at least one object identified by the attribute attached to the reference data item. 
     With this structure, when attaching an attribute to the plurality of contents (images), the information processing device uses images (reference data items) to which attributes are already attached. By attaching the attribute to the plurality of contents, the contents can also be used as reference data items. 
     (4) Each object included in each reference data item may be associated with a degree of reliability used in object identification, and when two or more objects are targeted for acquisition from a same reference data item, the second acquisition unit may acquire the object associated with a highest degree of reliability. 
     With this structure, the information processing device acquires the object with the highest degree of reliability. The attribute attached to the plurality of contents therefore also has a high degree of reliability with respect to the plurality of contents. 
     (5) Each reference data item may be a shot image, the attribute attached to each reference data item may indicate a shooting location, and based on the shooting locations indicated by the attributes acquired from the reference data items, the specification unit may organize the attributes into groups each of a predetermined range and specify the group containing a greatest number of attributes, the range indicated by the specified group being the specified attribute used for identifying the plurality of contents. 
     With this structure, the information processing device uses attributes that indicate shooting location. The information processing device can therefore attach an attribute indicating a shared location to the plurality of contents. 
     (6) Each reference data item may be a shot image, the attribute attached to each reference data item may indicate a shooting date and time, and based on the shooting dates and times indicated by the attributes acquired from the reference data items, the specification unit may organize the attributes into groups each of a predetermined time frame and specify the group containing a greatest number of attributes, the time frame of the specified group being the specified attribute used for identifying the plurality of contents. 
     With this structure, the information processing device uses attributes that indicate a shooting date and time. The information processing device can therefore attach an attribute indicating a shared time frame to the plurality of contents. 
     (7) Each reference data item may be a shot image, the attribute attached to each reference data item may be a keyword containing a description of the reference data item, and the specification unit may specify the number of occurrences of a same or equivalent keyword in the keywords indicated by the attributes acquired from the reference identifiers, and when the specified number of occurrences equals at least a predetermined number, use the keyword with at least the predetermined number of occurrences as the specified attribute used for identifying the plurality of contents. 
     With this structure, the information processing device uses a keyword containing a description of the content as the attribute. The information processing device can therefore attach a shared keyword to the plurality of contents as the attribute. 
     (8) The plurality of reference data items may constitute dictionary data, and for each acquired object in each content acquired by the first acquisition unit, the second acquisition unit may acquire the one or more similar objects by searching among the dictionary data. 
     With this structure, the information processing device uses dictionary data and can therefore easily acquire objects. 
     (9) The information processing device may further comprise a user information storage unit storing user information pertaining to a user, wherein the second acquisition unit acquires, for each acquired object in each content acquired by the first acquisition unit, the one or more similar objects in association with the user, and the specification unit acquires, for each of the one or more similar objects, the attribute that identifies the object and is attached to the reference data item that includes the object, and specifies, using each acquired attribute and the user information, a time period in which each of the plurality of contents was shot, the specified time period being the specified attribute for each of the plurality of contents. 
     With this structure, the information processing device specifies a time period for the plurality of contents based on user information and attributes of objects related to the user. The information processing device can therefore acquire an appropriate time period for the plurality of contents. 
     (10) The user information may be a date of birth of the user, the attribute may indicate an age range of the user, the object included in each reference data item may be a face of the user estimated to correspond to the age range indicated by the attribute attached to the reference data item, and the specification unit may specify, using the age range indicated by each acquired attribute and the date of birth of the user, a time period in which each of the plurality of contents was shot. 
     With this structure, the information processing device uses the date of birth of the user as the user information. The information processing device can therefore easily specify an appropriate time period for the plurality of contents. 
     INDUSTRIAL APPLICABILITY 
     The information processing device according to the present invention estimates and attaches the most appropriate attribute to a plurality of contents forming an input content group and allows for management and effective use of the contents based on the attached attribute. Furthermore, the information processing device according to the present invention can also recommend a content related to another content. 
     REFERENCE SIGNS LIST 
     
         
         
           
               10  information processing device 
               101  content storage unit 
               102  content group selection unit 
               103  database 
               104  object selection unit 
               105  content analysis unit 
               106  similar object specification unit 
               107  accompanying data extraction unit 
               108  accompanying data estimation unit 
               109  accompanying data writing unit 
               110  receiving unit