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.
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. 2. The method of claim 1 , wherein generating a score for each of a plurality of images comprises: identifying, from a corpus of images, an image that has not been scored using the trained image relevance model for the first query; assigning the image a relevance score specifying a measure of relevance of the image as a search result for the first query; storing the relevance score in association with a reference to the image; and iteratively repeating the identifying, assigning, and storing for one or more additional images that have not been scored using the trained image relevance model for the first query.