1. A method comprising: receiving input that identifies an example image for use in querying a collection of digital images; using local and global feature descriptors to automatically rank the collection of digital images with respect to similarity to the example image, wherein each local feature descriptor represents a portion of an image based on a division of the image into multiple portions, and wherein each global feature descriptor represents an image as a whole; using a final classifier and multiple different intermediate classifiers to perform the automatic ranking, wherein: the different intermediate classifiers generate intermediate relevance metrics with respect to different modalities; the final classifier blends results from the intermediate classifiers into a final relevance metric to be used for displaying images in ranked order; after generating the final relevance metric, receiving input identifying a second example image for use in querying the collection of digital images; automatically determining at least one new intermediate classifier, based at least in part on the example image; automatically determining a new final classifier, based at least in part on the example image; and using the new intermediate classifier and the new final classifier to automatically re-rank the collection of digital images with respect to similarity to the example image.
1. A method comprising: receiving input that identifies an example image for use in querying a collection of digital images; using local and global feature descriptors to automatically rank the collection of digital images with respect to similarity to the example image, wherein each local feature descriptor represents a portion of an image based on a division of the image into multiple portions, and wherein each global feature descriptor represents an image as a whole; using a final classifier and multiple different intermediate classifiers to perform the automatic ranking, wherein: the different intermediate classifiers generate intermediate relevance metrics with respect to different modalities; the final classifier blends results from the intermediate classifiers into a final relevance metric to be used for displaying images in ranked order; after generating the final relevance metric, receiving input identifying a second example image for use in querying the collection of digital images; automatically determining at least one new intermediate classifier, based at least in part on the example image; automatically determining a new final classifier, based at least in part on the example image; and using the new intermediate classifier and the new final classifier to automatically re-rank the collection of digital images with respect to similarity to the example image. 7. A method according to claim 1 , wherein each digital image comprises a set of pixel values, the method further comprising: computing multiple local feature descriptors for each digital image, wherein the different local feature descriptors for each image are based on the pixel values in different portions of that image.