Supervised re-ranking for visual search
AssigneeMicrosoft Corporation
InventorsLinjun Yang, Xian-Sheng Hua
Life of this patent
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Abstract
Supervised re-ranking for visual search may include re-ordering images that are returned in response to a text-based image search by exploiting visual information included in the images. In one example, supervised re-ranking for visual search may include receiving a textual query, obtaining an initial ranking result including a plurality of images corresponding to the textual query, and representing the textual query by a visual context of the plurality of images. A query-independent re-ranking model may be trained based on visual re-ranking features of the plurality of images of the textual query in accordance with a supervised training algorithm.
Claims20
A computer-implemented method comprising:
- under control of one or more processors configured with executable instructions:
- receiving a textual query;
- obtaining an initial ranking result corresponding to the textual query, the initial ranking result including a plurality of images corresponding to the textual query;
- representing the textual query using a visual context of the plurality of images, the visual context facilitating determining the visual re-ranking features including prior re-ranking features, contextual re-ranking features and pseudo relevance feedback features;
- training, using a supervised training algorithm, a query-independent re-ranking model including a weighted linear combination of features based at least upon the visual re-ranking features of the plurality of images of the textual query to obtain a plurality of weights associated with the features of the weighted linear combination.
A computer-implemented method comprising:
- under control of one or more processors configured with executable instructions:
- receiving a textual query from a client;
- obtaining an initial ranking result related to the textual query, the initial ranking result including a plurality of images;
- representing the textual query in terms of a visual context of at least a subset of the plurality of images;
- re-ranking the initial ranking result based at least upon visual re-ranking features of the at least subset of the plurality of images and a query-independent re-ranking model, the visual re-ranking features being determined from the visual context, and the query-independent re-ranking model being trained according to a supervised algorithm using visual re-ranking features of a plurality of labeled images corresponding to one or more training textual queries.
One or more computer-readable media storing computer-executable instructions that, when executed by one or more processors, cause the one or more processors to perform acts comprising:
- under control of one or more processors configured with executable instructions:
- obtaining an initial ranking result corresponding to a textual query, the initial ranking result including a plurality of images corresponding to the textual query;
- and
- re-ranking the initial ranking result based at least upon initial rankings of the plurality of images in the initial ranking result, a neighborhood structure of the plurality of images and pseudo relevance feedback of a predetermined number of top-ranked images in the initial ranking result.
Dependencies are inferred from the claim text.