Visual recognition using social links
First Claim
1. 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.
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Abstract
System, method and architecture for providing improved visual recognition by modeling visual content, semantic content and an implicit social network representing individuals depicted in a collection of content, such as visual images, photographs, etc., which network may be determined based on co-occurrences of individuals represented by the content, and/or other data linking the individuals. In accordance with one or more embodiments, using images as an example, a relationship structure may comprise an implicit structure, or network, determined from co-occurrences of individuals in the images. A kernel jointly modeling content, semantic and social network information may be built and used in automatic image annotation and/or determination of relationships between individuals, for example.
12 Citations
20 Claims
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1. A method comprising:
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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. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. 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:
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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. - View Dependent Claims (13, 14, 15, 16, 17, 18, 19)
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20. A computing device comprising:
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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.
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Specification