Systems and methods for generating a high-level visual vocabulary
AssigneeCanon Kabushiki Kaisha
InventorsYang Yang, Bradley Scott Denney, Juwei Lu, Dariusz Dusberger, Hung Khei Huang
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Abstract
Systems and methods for learning a high-level visual vocabulary generate inter-visual-word relationships between a plurality of visual words based on visual word-label relationships, map the visual words to a vector space based on the inter-visual word relationships, and generate high-level visual words in the vector space.
Claims19
A method comprising:
- generating inter-visual-word relationships between a plurality of visual words based on visual word-label relationships;
- mapping the visual words to a vector space based on the inter-visual-word relationships;
- and
- generating high-level visual words in the vector space.
A device comprising:
- one or more computer-readable media configured to store instructions;
- and
- one or more processors configured to cause the device to
- generate mid-level visual words based on a plurality of low-level features that were extracted from images;
- generate inter-mid-level visual-word relationships based on mid-level visual-word-label relationships;
- and
- map the mid-level visual words to a vector space based on the inter-mid-level visual-word relationships.
One or more computer-readable media storing instructions that, when executed by one or more computing devices, cause the one or more computing devices to perform operations comprising:
- generating inter-visual-word relationships between a plurality of visual words based on visual word-label relationships;
- mapping the visual words to a vector space based on the inter-visual-word relationships;
- and
- generating high-level visual words in the vector space based on respective positions of the visual words in the vector space.
A method comprising:
- generating inter-visual-word relationships between a plurality of visual words;
- generating sets of importance weights for the visual words, wherein a set of importance weights includes a respective weight for each of the visual words;
- mapping the visual words to a plurality of vector spaces based on the inter-visual word relationships and on the sets of importance weights, wherein each vector space corresponds to a respective one of the sets of importance weights;
- and
- generating high-level visual words in the plurality of vector spaces.
Dependencies are inferred from the claim text.