1. A method comprising: identifying a set of training images previously used to train an image relevance model that generates relevance measures of images to a query based on content feature values of the images, the query being a unique set of one or more query terms; identifying typical visual features for the set of training images, including: for two or more visual features: determining that at least a given portion of the training images from the set of training images that were used to train the image relevance model each include the visual features, and in response to determining that at least a given portion of the training images each include the visual features, identifying the visual features as typical visual features for the set of training images; identifying, as a subset of training images from the set of training images, training images having at least a threshold portion of the identified typical visual features, wherein the subset does not include all images within the set of training images; assigning, to each training image in the subset of the training images, a weight that increases in proportion to the number of the identified typical visual features that the training image has, wherein each training image in the subset has a higher weight than weights assigned to other images from the set of training images that are not in the subset of training images; and generating a re-trained image relevance model based on visual features of the set of training images and the weights assigned to the training images in the subset of training images that have at least the threshold portion of the identified typical visual features.
1. A method comprising: identifying a set of training images previously used to train an image relevance model that generates relevance measures of images to a query based on content feature values of the images, the query being a unique set of one or more query terms; identifying typical visual features for the set of training images, including: for two or more visual features: determining that at least a given portion of the training images from the set of training images that were used to train the image relevance model each include the visual features, and in response to determining that at least a given portion of the training images each include the visual features, identifying the visual features as typical visual features for the set of training images; identifying, as a subset of training images from the set of training images, training images having at least a threshold portion of the identified typical visual features, wherein the subset does not include all images within the set of training images; assigning, to each training image in the subset of the training images, a weight that increases in proportion to the number of the identified typical visual features that the training image has, wherein each training image in the subset has a higher weight than weights assigned to other images from the set of training images that are not in the subset of training images; and generating a re-trained image relevance model based on visual features of the set of training images and the weights assigned to the training images in the subset of training images that have at least the threshold portion of the identified typical visual features. 5. The method of claim 1 , further comprising assigning a multiplier value to each image in the subset of training images, the multiplier value being greater than one.