Visual recognition using social links

A method comprising:

• training, by at least one computing device, a machine-modeled kernel jointly modeling content, semantic information and social network information, the training comprising building the kernel using a content kernel trained using a training set comprising content feature information, a semantic kernel trained using semantic feature information of the training data set and a social network kernel trained using social network feature information of the training data set, an implicit social network determined from a plurality of content items is used in determining the social network feature information used in training the kernel;
• identifying, by the at least one computing device and using a number of content items other than the plurality of content items, a plurality of individuals depicted in the number of content items using the kernel;
• identifying, by the at least one computing device, a number of relationships, each relationship being between two individuals, of the plurality of individuals, identified in a same content item, of the number of content items, using the kernel;
• and
• representing, by the at least one computing device, each relationship, of the number of identified relationships, in an electronic social network comprising a plurality of nodes and a plurality of connections, each identified relationship being represented as a connection between a pair of individuals, of the plurality of individuals, identified in the same content item using the kernel and each individual of the pair being represented as a node of the plurality of nodes.

A non-transitory computer-readable storage medium tangibly encoded with computer-executable instructions that when executed by a processor associated with a computing device perform a method comprising:

• training a machine-modeled kernel jointly modeling content, semantic information and social network information, the training comprising building the kernel using a content kernel trained using a training set comprising content feature information, a semantic kernel trained using semantic feature information of the training data set and a social network kernel trained using social network feature information of the training data set, an implicit social network determined from a plurality of content items is used in determining the social network feature information used in training the kernel;
• identifying, using a number of content items other than the plurality of content items, a plurality of individuals depicted in the number of content items using the kernel;
• identifying a number of relationships, each relationship being between two individuals, of the plurality of individuals, identified in a same content item, of the number of content items, using the kernel;
• and
• representing each relationship, of the number of identified relationships, in an electronic social network comprising a plurality of nodes and a plurality of connections, each identified relationship being represented as a connection between a pair of individuals, of the plurality of individuals, identified in the same content item using the kernel and each individual of the pair being represented as a node of the plurality of nodes.

A computing device comprising:

• a processor;
• a non-transitory storage medium for tangibly storing thereon program logic for execution by the processor, the program logic comprising:
• training logic executed by the processor for training a machine-modeled kernel jointly modeling content, semantic information and social network information, the training comprising building the kernel using a content kernel trained using a training set comprising content feature information, a semantic kernel trained using semantic feature information of the training data set and a social network kernel trained using social network feature information of the training data set, an implicit social network determined from a plurality of content items is used in determining the social network feature information used in training the kernel;
• identifying logic executed by the processor for identifying, using a number of content items other than the plurality of content items, a plurality of individuals depicted in the number of content items using the kernel;
• identifying logic executed by the processor for identifying a number of relationships, each relationship being between two individuals, of the plurality of individuals, identified in a same content item, of the number of content items, using the kernel;
• and
• representing logic executed by the processor for representing each relationship, of the number of identified relationships, in an electronic social network comprising a plurality of nodes and a plurality of connections, each identified relationship being represented as a connection between a pair of individuals, of the plurality of individuals, identified in the same content item using the kernel and each individual of the pair being represented as a node of the plurality of nodes.