Image relevance model
First Claim
1. A computer-implemented method, comprising:
- identifying, as positive images, a first set of images that are considered relevant to a first query based, at least in part, on a first selection rate for images in the first set when presented as search results for the first query;
identifying, as negative images, a second set of images that differ from the first set of images and that are considered relevant to a different query based, at least in part, on a second selection rate for images in the second set when presented as search results for the different query;
training an image relevance model for the first query based on feature values of the positive images and feature values of the negative images;
generating a score for each of a plurality of images based on feature values of the plurality of images and the image relevance model;
receiving a search query;
identifying, from the plurality of images, one or more highest scoring images based on the score for each of one or more of the plurality of images; and
providing at least a portion of the one or more highest scoring images as results for the search query.
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Accused Products
Abstract
Methods, systems, and apparatus, including computer program products, for identifying images relevant to a query are disclosed. An image search subsystem selects images to reference in image search results that are responsive to a query based on an image relevance model that is trained for the query. An independent image relevance model is trained for each unique query that is identified by the image search subsystem. The image relevance models can be applied to images to order image search results obtained for the query. Each relevance model is trained based on content feature values of images that are identified as being relevant to the query (e.g., frequently selected from the image search results) and images that are identified as being relevant to another unique query. The trained model is applied to the content feature values of all known images to generate an image relevance score that can be used to order search results for the query.
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Citations
20 Claims
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1. A computer-implemented method, comprising:
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identifying, as positive images, a first set of images that are considered relevant to a first query based, at least in part, on a first selection rate for images in the first set when presented as search results for the first query; identifying, as negative images, a second set of images that differ from the first set of images and that are considered relevant to a different query based, at least in part, on a second selection rate for images in the second set when presented as search results for the different query; training an image relevance model for the first query based on feature values of the positive images and feature values of the negative images; generating a score for each of a plurality of images based on feature values of the plurality of images and the image relevance model; receiving a search query; identifying, from the plurality of images, one or more highest scoring images based on the score for each of one or more of the plurality of images; and providing at least a portion of the one or more highest scoring images as results for the search query. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A system, comprising:
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a data store storing images; one or more processors that interact with the data store and execute instructions that cause the one or more processors to perform operations comprising; identifying, from the data store and as positive images, a first set of images that are considered relevant to a first query based, at least in part, on a first selection rate for images in the first set when presented as search results for the first query; identifying, from the data store and as negative images, a second set of images that differ from the first set of images and that are considered relevant to a different query based, at least in part, on a second selection rate for images in the second set when presented as search results for the different query; training an image relevance model for the first query based on feature values of the positive images and feature values of the negative images; generating a score for each of a plurality of images based on feature values of the plurality of images and the image relevance model; receiving a search query; identifying, from the plurality of images, one or more highest scoring images based on the score for each of one or more of the plurality of images; and providing at least a portion of the one or more highest scoring images as results for the search query. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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15. A non-transitory computer readable medium storing instructions that when executed by one or more data processing apparatus cause the one or more data processing apparatus to perform operations comprising:
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identifying, as positive images, a first set of images that are considered relevant to a first query based, at least in part, on a first selection rate for images in the first set when presented as search results for the first query; identifying, as negative images, a second set of images that differ from the first set of images and that are considered relevant to a different query based, at least in part, on a second selection rate for images in the second set when presented as search results for the different query; training an image relevance model for the first query based on feature values of the positive images and feature values of the negative images; generating a score for each of a plurality of images based on feature values of the plurality of images and the image relevance model; receiving a search query; identifying, from the plurality of images, one or more highest scoring images based on the score for each of one or more of the plurality of images; and providing at least a portion of the one or more highest scoring images as results for the search query. - View Dependent Claims (16, 17, 18, 19, 20)
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Specification