USING A BIPARTITE GRAPH TO MODEL AND DERIVE IMAGE AND TEXT ASSOCIATIONS
First Claim
1. A method comprising:
- constructing, based on a set of data, a bipartite graph;
wherein the set of data comprises a set of visual words, a set of linguistic words, and a set of associations between the set of visual words and the set of linguistic words;
wherein each association in the set of associations is an association between a visual word in the set of visual words and a linguistic word in the set of linguistic words;
wherein constructing the bipartite graph includes constructing a first partition and a second partition;
wherein the first partition comprises a first set of nodes;
wherein each node in the first set of nodes represents a linguistic word in the set of linguistic words;
wherein the second partition comprises a second set of nodes;
wherein each node in the second set of nodes represents a visual word in the set of visual words;
deriving and storing in volatile or non-volatile memory, based on the bipartite graph, an association score between a first node of said bipartite graph and a second node of said bipartite graph.
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Abstract
A method for deriving probabilistic association scores based on image content is provided. A bipartite graph is constructed based on a database of image content and associated textual content. One partition of the bipartite graph contains image content and the other partition of the bipartite graph contains textual content. Weighted edges between nodes in the two partitions represent associations between the image content and textual content in the database. Random walks on the bipartite graph are performed to derive probabilistic association scores between image content and textual content. Association scores are used to automatically annotate images and detect spurious image tags.
13 Citations
22 Claims
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1. A method comprising:
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constructing, based on a set of data, a bipartite graph; wherein the set of data comprises a set of visual words, a set of linguistic words, and a set of associations between the set of visual words and the set of linguistic words; wherein each association in the set of associations is an association between a visual word in the set of visual words and a linguistic word in the set of linguistic words; wherein constructing the bipartite graph includes constructing a first partition and a second partition; wherein the first partition comprises a first set of nodes; wherein each node in the first set of nodes represents a linguistic word in the set of linguistic words; wherein the second partition comprises a second set of nodes; wherein each node in the second set of nodes represents a visual word in the set of visual words; deriving and storing in volatile or non-volatile memory, based on the bipartite graph, an association score between a first node of said bipartite graph and a second node of said bipartite graph. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A computer-readable storage medium storing instructions, the instructions including instructions which, when executed by one or more processors, cause the one or more processors to perform the steps of:
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constructing, based on a set of data, a bipartite graph; wherein the set of data comprises a set of visual words, a set of linguistic words, and a set of associations between the set of visual words and the set of linguistic words; wherein each association in the set of associations is an association between a visual word in the set of visual words and a linguistic word in the set of linguistic words; wherein constructing the bipartite graph includes constructing a first partition and a second partition; wherein the first partition comprises a first set of nodes; wherein each node in the first set of nodes represents a linguistic word in the set of linguistic words; wherein the second partition comprises a second set of nodes; wherein each node in the second set of nodes represents a visual word in the set of visual words; deriving and storing in volatile or non-volatile memory, based on the bipartite graph, an association score between a first node of said bipartite graph and a second node of said bipartite graph. - View Dependent Claims (13, 14, 15, 16, 17, 18, 19, 20, 21, 22)
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Specification