Patent Publication Number: US-10318840-B2

Title: Generating a group photo collection

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of U.S. patent application Ser. No. 13/895,742, entitled “GENERATING A GROUP PHOTO COLLECTION,” filed May 16, 2013, which claims priority to Provisional application No. 61/648,498 entitled “GENERATING A GROUP PHOTO COLLECTION,” filed May 17, 2012, which is hereby incorporated by reference as if set forth in full in this application for all purposes. 
    
    
     BACKGROUND 
     Social network systems often enable users to upload photos and create photo albums that contain the uploaded photos. After a user uploads photos to a social network system, the social network system typically enables the user to create one or more photo albums. The user can then determine which photos to include in each of the photo albums. The social network system typically enables the user to share the photo albums with other users of the social network system. For example, a user may allow other users to access and view photos in particular photo albums. 
     SUMMARY 
     Implementations generally relate to generating a group photo collection. In some implementations, a method includes determining a plurality of users in a specified group of users of a social network system. The method also includes receiving photos associated with the users. The method also includes providing an interface enabling the plurality of users to collaborate in creating a group photo collection, where the group photo collection includes the plurality of photos. The method also includes providing one or more recommendations to create a photo album based on one or more themes, where the one or more themes are based on patterns of objects recognized in the plurality of photos. 
     With further regard to this method, in some implementations, the determining of the plurality of users may include receiving an indication from a user who creates a group photo collection as to which other users are in the specified group of users. In some implementations, the determining of the plurality of users may include recommending users to be added to the specified group of users. In some implementations, the method further includes enabling each user of the plurality of users to designate other users to be added to the specified group of users. In some implementations, the method further includes enabling the users to collaborate to create shared or common photo albums. In some implementations, to enable the plurality of users to collaborate, the method further includes enabling the users to collaborate to create shared or common photo albums, and where the users have privileges to create, label, and modify photo albums in the group photo collection. In some implementations, to enable the plurality of users to collaborate, the method further includes one or more of enabling the users to collaborate in order to cluster similar photos together in any one or more photo albums, enabling the users to order the photos, enabling the users to edit the photos, and enabling the users to add captions to the photos. In some implementations, the recommending is based on themes of color. In some implementations, the recommending is based on events. In some implementations, the recommending is based on time. 
     In another implementation, a method includes determining a plurality of users in a specified group of users of a social network system. In some implementations, the determining includes receiving an indication from a user who creates a group photo collection as to which other users are in the specified group of users. The method also includes receiving photos associated with the users, where the photos are received independently from each of the users. The method also includes providing an interface enabling the plurality of users to collaborate in creating the group photo collection, where the group photo collection includes the plurality of photos, and where the users have privileges to create, label, and modify photo albums in the group photo collection. The method also includes providing one or more recommendations to create a photo album based on one or more themes, where the one or more themes are based on patterns of objects recognized in the plurality of photos. 
     In another implementation, a system includes one or more processors, and logic encoded in one or more tangible media for execution by the one or more processors. When executed, the logic is operable to perform operations including: determining a plurality of users in a specified group of users of a social network system; receiving photos associated with the users; enabling the plurality of users to collaborate in creating a group photo collection, where the group photo collection includes the plurality of photos; and providing one or more recommendations to create a photo album based on one or more themes, where the one or more themes are based on patterns of objects recognized in the plurality of photos. 
     With further regard to this system, in some implementations, the determining of the plurality of users may include receiving an indication from a user who creates a group photo collection as to which other users are in the specified group of users. In some implementations, the logic when executed is further operable to perform operations including recommending users to be added to the specified group of users. In some implementations, the logic when executed is further operable to perform operations including enabling each user of the plurality of users to designate other users to be added to the specified group of users. In some implementations, the logic when executed is further operable to perform operations including providing an interface enabling the users to collaborate to create shared or common photo albums. In some implementations, the logic when executed is further operable to perform operations including enabling the users to collaborate to create shared or common photo albums, and where the users have privileges to create, label, and modify photo albums in the group photo collection. In some implementations, the logic when executed is further operable to perform operations including enabling the users to collaborate in order to cluster similar photos together in any one or more photo albums, enabling the users to order the photos, enabling the users to edit the photos, and enabling the users to add captions to the photos. In some implementations, the logic when executed is further operable to perform operations including recommending creating photo albums based on themes of color. In some implementations, the logic when executed is further operable to perform operations including recommending creating photo albums based on events. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  illustrates a block diagram of an example network environment, which may be used to implement the embodiments described herein. 
         FIG. 2  illustrates an example simplified flow diagram for generating a group photo collection, according to some implementations. 
         FIG. 3  illustrates a block diagram of an example server device, which may be used to implement the implementations described herein. 
     
    
    
     DETAILED DESCRIPTION 
     Implementations described herein enable users to collaborate in creating a group photo collection. In some implementations, a system determines users of the social network system who are contributors to the group photo collection. The system receives photos associated with the users. For example, each of the users in a specified group of users may provide photos to the system. The system enables the users to collaborate in creating the group photo collection. In various implementations, the system may also recommend creating photo albums based on one or more factors. For example, the system may make recommendations to create photo albums based on themes, based on events, and/or based on time. 
       FIG. 1  illustrates a block diagram of an example network environment  100 , which may be used to implement the implementations described herein. In some implementations, network environment  100  includes a system  102 , which includes a server device  104  and a social network database  106 . In various implementations, the term system  102  and phrase “social network system” may be used interchangeably. Network environment  100  also includes client devices  110 ,  120 ,  130 , and  140 , which may communicate with each other via system  102  and a network  150 . 
     For ease of illustration,  FIG. 1  shows one block for each of system  102 , server device  104 , and social network database  106 , and shows four blocks for client devices  110 ,  120 ,  130 , and  140 . Blocks  102 ,  104 , and  106  may represent multiple systems, server devices, and social network databases. Also, there may be any number of client devices. In other implementations, network environment  100  may not have all of the components shown and/or may have other elements including other types of elements instead of, or in addition to, those shown herein. 
     In various implementations, users U 1 , U 2 , U 3 , and U 4  may collaborate with each other in building a group photo collection using respective client devices  110 ,  120 ,  130 , and  140 . 
       FIG. 2  illustrates an example simplified flow diagram for generating a group photo collection, according to some implementations. In various implementations, system  102  may generate a group photo collection in a social network system, or anywhere visual media may be used and/or viewed. Referring to both  FIGS. 1 and 2 , a method is initiated in block  202 , where system  102  determines a group of users in a specified group of users of the social network system who will collaborate to build a group photo collection. For example, users U 1 , U 2 , U 3 , and U 4  may be collaborators in building the group photo collection. In some implementations, system  102  may receive an indication from a user who initiates or creates the group photo collection as to which other users are in the specified group of users. In some implementations, system  102  may recommend users to be added as collaborators based on social network commonalities (e.g., being social network friends, having similar interests, etc.). 
     In various implementations, a group photo collection may be a collection of photos, which may be arranged in one or more photo albums. The photos in the group photo collection are provided by different users in a specified group of users. The terms “users” and “collaborators” may be used interchangeably. 
     For ease of illustration, four example users U 1 , U 2 , U 3 , and U 4  are described. There may be any number of users collaborating to build a group photo collection. Also, in some implementations, system  102  may enable users who are original designated collaborators to designate other users to be added as collaborators. 
     In block  204 , system  102  receives photos associated with the users. For example, system  102  may receive one or more photos from each of users U 1 , U 2 , U 3 , and U 4  via respective client devices  110 ,  120 ,  130 , and  140 . 
     In various implementations, system  102  may obtain photos independently from each of the users U 1 , U 2 , U 3 , and U 4 , where the photos obtained from different users need not be associated by any particular time period or event. For example, user U 1  may contribute photos obtained from a wedding, user U 2  may contribute photos a subsequent month from a family gathering, etc. 
     In block  206 , system  102  enables the users to collaborate in creating the group photo collection, where system  102  enables the users to participate in a variety of collaborative tasks. For example, in some implementations, system  102  enables the users to pool photos, where the photos are to be included in the group photo collection. In various implementations, system  102  may provide an interface that enables the users to collaborate. In some implementations, the interface may be shared among multiple users, and may provide the users with access to photos that the users may use to collaborate in creating photo albums. 
     In some implementations, system  102  enables the users to collaborate in order to create shared or common photo albums, where the users have privileges to create, label, and modify photo albums in the group photo collection. For example, any of the users U 1 , U 2 , U 3 , and U 4  may create a particular photo album, any of the users U 1 , U 2 , U 3 , and U 4  may label the photo album, and any of the users U 1 , U 2 , U 3 , and U 4  may modify the photo album. 
     In some implementations, system  102  enables users to collaborate to cluster similar photos together in any one or more photo albums, enables users to order the photos, enables users to edit the photos, enables users to delete photos, and enables users to add captions to the photos, etc. Users U 1 , U 2 , U 3 , and U 4  may collaborate with each other in building a group photo collection using respective client devices  110 ,  120 ,  130 , and  140 . In various implementations, users U 1 , U 2 , U 3 , and U 4 , and any newly added collaborators may access and contribute to the group photo collection via network  150  and may curate photos from social network database  106 . 
     In some implementations, system  102  may make recommendations to the users with regard to adding photos to particular photo albums in the group photo collection and with regard to creating and organizing photo albums. In various implementations, these recommendations may be based on one or more criteria. 
     In some implementations, system  102  may recommend creating photo albums based on events. For example, system  102  may detect that two or more users are attending the same event, in which case system  102  may recommend that the users add photos from the event to the group photo collection. In other words, system  102  may recommend a photo album having a particular event theme, etc. In various implementations, system  102  may perform recognition algorithms to determine which photos are related with respect to an event. For example, system  102  may determine that two or more of the collaborators are at a gathering (e.g., via a check-in, an event registration process, etc.). System  102  may also recognize two or more of the collaborators from photos captured at the event which were immediately uploaded to system  102 . In some implementations, system  102  may determine that photos provided by the users are from the same event based on similar subject matter (e.g., people, landmarks, objects, etc.) and based on the photos being captured within the same time period (e.g., within several hours, during the same day, etc.). 
     For example, in some implementations, system  102  may recommend creating photo albums based on themes. In some implementations, system  102  may group the photos into photo albums based on the themes. As described in more detail below, such themes may involve various patterns of attributes and/or patterns of objects detected among photos. 
     In some implementations, system  102  may detect color themes, where a number of photos in the group photo collection may have a dominant color (e.g., blue, green, red, etc.). In some implementations, system  102  may use a recognition algorithm to detect patterns of one or more colors in multiple photos. Based on the detection of color patterns, system  102  may recommend grouping photos having the detected patterns of colors into one or more photo albums. 
     In some implementations, system  102  may detect other themes based on objects (e.g., pets, landmarks, etc.). System  102  may recommend grouping like photos into photo albums based on such themes. Example implementations for recognizing themes are described in more detail below. In some implementations, system  102  may use a recognition algorithm to detect patterns of one or more objects in multiple photos. Based on the detection of patterns of objects, system  102  may recommend grouping photos having the detected patterns of objects into one or more photo albums. 
     In some implementations, system  102  may associate themes of color and/or objects with various events. Such events may include, for example, special events such a weddings, graduation ceremonies, etc. In an example scenario, system  102  may detect a cake in multiple photos. System  102  may also detect the same two people in the same photo with the cake. System  102  may also detect a vale and dress on one of the two people. System  102  may also detect the words “wedding” or “marriage” or “ceremony” in one or more photos (e.g., “marriage ceremony” on a wedding invitation). 
     In some implementations, system  102  may apply location and/or time parameters when detecting themes. System  102  may determine time and location using time stamps and location identifications (e.g., place ID). For example, system  102  may detect themes in photos taken at a particular location. A combination of the location and themes may indicate a special event. For example, system  102  detecting a vale and a white dress on one person standing next to another person at a church may be indicative of a wedding ceremony. As such, system  102  may recommend including such photos in one or more photo albums (e.g., wedding photo album). 
     In some implementations, system  102  may detect themes in photos taken within a predetermined time period (e.g., a 48 hour window). Such time parameters indicate particular categories of events. For example, system  102  detecting the same group of people over smaller time period (e.g., 3 hours) may indicate a gathering or party depending on the size of the group. System  102  detecting the same group of people over a larger time period (e.g. 2 days) may indicate a reunion (e.g., family reunion). As such, system  102  may recommend including such photos in one or more photo albums. 
     In various implementations, system  102  enables users of the social network system to specify and/or consent to the use of personal information, which may include the system  102  using their faces in photos or using their identity information in recognizing people identified in photos. For example, system  102  may provide users with multiple selections directed to specifying and/or consenting to the use of personal information. For example, selections with regard to specifying and/or consenting may be associated with individual photos, all photos, individual photo albums, all photo albums, etc. The selections may be implemented in a variety of ways. For example, system  102  may cause buttons or check boxes to be displayed next to various selections. In some implementations, system  102  enables users of the social network to specify and/or consent to the use of using their photos for face matching and/or facial recognition in general. Example implementations for recognizing faces and other objects are described in more detail below. 
     In some implementations, system  102  may recommend creating photo albums based on locations. For example, system  102  may detect a particular location in various photos. System  102  may detect locations based on geotagging, landmark recognition, or any other suitable means. For example, user U 1  visits a location such as Paris, France, and to capture a number of photos; and user U 2  also visited Paris, France, and captures a number of photos. System  102  may detect the common location and recommend grouping photos captured at that location, even if the trips were unrelated or occurred at different times. For example, system  102  may recommend a photo album having a location theme, a travel theme, etc. 
     In some implementations, system  102  may recommend creating photo albums based on time. For example, system  102  may detect a number of photos captured during a particular time period such as a holiday (e.g., Thanksgiving Day, etc.) and may recommend making photo albums based on the time period. For example, system  102  may recommend a photo album having a holiday theme, etc. 
     In some implementations, system  102  may recommend creating photo albums having any combination of themes (e.g., color, event, location, time, etc.). For example, system  102  may detect that photos are associated with an event such as a wedding, and also detect particular clusters of photos that revolve around particular activities (e.g., exchange of wedding vows, cake cutting, etc.). System  102  may recommend photo albums based on a combination of any one or more of these activities. 
     In some implementations, system  102  may display the group photo collection in any number of locations. For example, system  102  may display the group photo collection in a single gallery on a group webpage that is separate from a particular user&#39;s personal webpage. System  102  may also display the group photo collection on an events webpage. System  102  may also display the group photo collection on one or more personal webpages of particular users. 
     In various implementations, system  102  may utilize a variety of recognition algorithms to recognize faces, themes, objects, etc. in photos. Such facial algorithms may be integral to system  102 . System  102  may also access recognition algorithms provided by software that is external to system  102 , and that system  102  accesses. 
     In various implementations, system  102  enables users of the social network system to specify and/or consent to using their faces in photos or using their identity information in recognizing people identified in photos. For example, system  102  may provide users with multiple selections for specifying and/or consenting to the use of personal information. For example, selections for specifying and/or consenting the use of personal information may be associated with individual photos, all photos, individual photo albums, all photo albums, etc. The selections may be implemented in a variety of ways. For example, system  102  may cause buttons or check boxes to be displayed next to various selections. In some implementations, system  102  enables users of the social network to specify and/or consent to the use their photos for face matching and/or facial recognition in general. 
     In situations in which the systems discussed here collect personal information about users, or may make use of personal information, the users may be provided with an opportunity to control whether programs or features collect user information (e.g., information about a user&#39;s social network, social actions or activities, profession, a user&#39;s preferences, or a user&#39;s current location), or to control whether and/or how to receive content from the content server that may be more relevant to the user. In addition, certain data may be treated in one or more ways before it is stored or used, so that personally identifiable information is removed. For example, a user&#39;s identity may be treated so that no personally identifiable information can be determined for the user, or a user&#39;s geographic location may be generalized where location information is obtained (such as to a city, ZIP code, or state level), so that a particular location of a user cannot be determined. Thus, the user may have control over how information is collected about the user and used by a content server. 
     In various implementations, system  102  obtains reference images of users of the social network system, where each reference image includes an image of a face that is associated with a known user. The user is known, in that system  102  has the user&#39;s identity information such as the user&#39;s name and other profile information. In some implementations, a reference image may be, for example, a profile image that the user has uploaded. In some implementations, a reference image may be based on a stored composite of a group of reference images. 
     In some implementations, to recognize a face in a photo, system  102  may compare the face (i.e., image of the face) and match the face to reference images of users of the social network system. Note that the term “face” and the phrase “image of the face” are used interchangeably. For ease of illustration, the recognition of one face is described in some of the example implementations described herein. These implementations may also apply to each face of multiple faces to be recognized. 
     In some implementations, system  102  may search reference images in order to identify any one or more reference images that are similar to the face in the photo. 
     In some implementations, for a given reference image, system  102  may extract features from the image of the face in a photo for analysis, and then compare those features to those of one or more reference images. For example, system  102  may analyze the relative position, size, and/or shape of facial features such as eyes, nose, cheekbones, mouth, jaw, etc. In some implementations, system  102  may use data gathered from the analysis to match the face in the photo to one more reference images with matching or similar features. In some implementations, system  102  may normalize multiple reference images, and compress face data from those images into a composite representation having information (e.g., facial feature data), and then compare the face in the photo to the composite representation for face matching and/or facial recognition. 
     In various implementations, system  102  may utilize either face matching or facial recognition, or both, depending on the particular implementation. In various implementations, face matching need not recognize faces to know they belong to the same person. Face matching (also referred to as face clustering) associates two faces as belonging to the same person, without necessarily identifying who that person is. In various implementations, facial recognition associates an identity (e.g., a name) with a face using existing face templates that have been identified (e.g., named). 
     In some scenarios, the face in the photo may be similar to multiple reference images associated with the same user. As such, there would be a high probability that the person associated with the face in the photo is the same person associated with the reference images. 
     In some scenarios, the face in the photo may be similar to multiple reference images associated with different users. As such, there would be a moderately high yet decreased probability that the person in the photo matches any given person associated with the reference images. To handle such a situation, system  102  may use various types of face matching and/or facial recognition algorithms to narrow the possibilities, ideally down to one best candidate. 
     For example, in some implementations, to facilitate in face matching and/or facial recognition, system  102  may use geometric face matching and/or facial recognition algorithms, which are based on feature discrimination. System  102  may also use photometric algorithms, which are based on a statistical approach that distills a facial feature into values for comparison. A combination of the geometric and photometric approaches could also be used when comparing the face in the photo to one or more references. 
     Other face matching and/or facial recognition algorithms may be used. For example, system  102  may use face matching and/or facial recognition algorithms that use one or more of principal component analysis, linear discriminate analysis, elastic bunch graph matching, hidden Markov models, and dynamic link matching. It will be appreciated that system  102  may use other known or later developed face matching and/or facial recognition algorithms, techniques, and/or systems. 
     In some implementations, system  102  may generate an output indicating a likelihood (or probability) that the face in the photo matches a given reference image. In some implementations, the output may be represented as a metric (or numerical value) such as a percentage associated with the confidence that the face in the photo matches a given reference image. For example, a value of 1.0 may represent 100% confidence of a match. This could occur, for example, when compared images are identical or nearly identical. The value could be lower, for example 0.5 when there is a 50% chance of a match. Other types of outputs are possible. For example, in some implementations, the output may be a confidence score for matching. 
     For ease of illustration, some example implementations described above have been described in the context of a face matching and/or facial recognition algorithms. Other similar recognition algorithms and/or visual search systems may be used to recognize objects such as landmarks, logos, entities, events, etc. in order to implement implementations described herein. 
     Although the steps, operations, or computations may be presented in a specific order, the order may be changed in particular implementations. Other orderings of the steps are possible, depending on the particular implementation. In some particular implementations, multiple steps shown as sequential in this specification may be performed at the same time. 
     While system  102  is described as performing the steps as described in the implementations herein, any suitable component or combination of components of system  102  or any suitable processor or processors associated with system  102  may perform the steps described. 
     Implementations described herein provide various benefits. For example, implementations enable multiple users to own and curate the same set of digital photos online or offline. Implementations described herein also increase overall engagement among end-users in a social networking environment. 
       FIG. 3  illustrates a block diagram of an example server device  300 , which may be used to implement the implementations described herein. For example, server device  300  may be used to implement server device  104  of  FIG. 1 , as well as to perform the method implementations described herein. In some implementations, server device  300  includes a processor  302 , an operating system  304 , a memory  306 , and an input/output (I/O) interface  308 . Server device  300  also includes a social network engine  310  and a media application  312 , which may be stored in memory  306  or on any other suitable storage location or computer-readable medium. Media application  312  provides instructions that enable processor  302  to perform the functions described herein and other functions. 
     For ease of illustration,  FIG. 3  shows one block for each of processor  302 , operating system  304 , memory  306 , I/O interface  308 , social network engine  310 , and media application  312 . These blocks  302 ,  304 ,  306 ,  308 ,  310 , and  312  may represent multiple processors, operating systems, memories, I/O interfaces, social network engines, and media applications. In other implementations, server device  300  may not have all of the components shown and/or may have other elements including other types of elements instead of, or in addition to, those shown herein. 
     Although the description has been described with respect to particular implementations thereof, these particular implementations are merely illustrative, and not restrictive. Concepts illustrated in the examples may be applied to other examples and implementations. 
     Note that the functional blocks, methods, devices, and systems described in the present disclosure may be integrated or divided into different combinations of systems, devices, and functional blocks as would be known to those skilled in the art. 
     Any suitable programming languages and programming techniques may be used to implement the routines of particular implementations. Different programming techniques may be employed such as procedural or object-oriented. The routines may execute on a single processing device or multiple processors. Although the steps, operations, or computations may be presented in a specific order, the order may be changed in different particular implementations. In some particular implementations, multiple steps shown as sequential in this specification may be performed at the same time. 
     A “processor” includes any suitable hardware and/or software system, mechanism or component that processes data, signals or other information. A processor may include a system with a general-purpose central processing unit, multiple processing units, dedicated circuitry for achieving functionality, or other systems. Processing need not be limited to a geographic location, or have temporal limitations. For example, a processor may perform its functions in “real-time,” “offline,” in a “batch mode,” etc. Portions of processing may be performed at different times and at different locations, by different (or the same) processing systems. A computer may be any processor in communication with a memory. The memory may be any suitable processor-readable storage medium, such as random-access memory (RAM), read-only memory (ROM), magnetic or optical disk, or other tangible media suitable for storing instructions for execution by the processor.