IMAGE TAGGING BASED UPON CROSS DOMAIN CONTEXT
First Claim
10-1. The method of claim 1, wherein the learned relationships are between one or more of people and events, locations and events, and people and locations.
2 Assignments
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Accused Products
Abstract
A method described herein includes receiving a digital image, wherein the digital image includes a first element that corresponds to a first domain and a second element that corresponds to a second domain. The method also includes automatically assigning a label to the first element in the digital image based at least in part upon a computed probability that the label corresponds to the first element, wherein the probability is computed through utilization of a first model that is configured to infer labels for elements in the first domain and a second model that is configured to infer labels for elements in the second domain. The first model receives data that identifies learned relationships between elements in the first domain and elements in the second domain, and the probability is computed by the first model based at least in part upon the learned relationships.
311 Citations
20 Claims
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10-1. The method of claim 1, wherein the learned relationships are between one or more of people and events, locations and events, and people and locations.
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13. A system comprising the following computer-executable components:
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an extractor component that receives a digital image extracts at least one feature from the digital image; and a label assignor component that automatically assigns a label to an element in the digital image, wherein the element corresponds to a first domain, and wherein the label assignor component assigns the label to the digital image based at least in part upon learned contextual relationships between elements in the first domain and elements in a second domain. - View Dependent Claims (14, 15, 16, 17, 18, 19)
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20. A computer-readable medium comprising instructions that, when executed by a processor, cause the processor to perform acts comprising:
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receiving a set of labels assigned to a collection of digital images by an individual; extracting one or more features from each of the digital images in the collection of digital images; learning relationships between elements in different domains, wherein each domain includes at least one element; inferring labels for elements in the domains; and outputting a recommendation to the individual of at least one label being assigned to at least one element in at least one digital image in the collection of digital images based at least in part upon the learned relationships.
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Specification