HEAT: Iterative Relevance Feedback with One Million Images
It has been shown repeatedly that iterative relevance feedback is a very efficient solution for content-based image retrieval. However, no existing system scales gracefully to hundreds of thousands or millions of images. We present a new approach dubbed Hierarchical and Expandable Adaptive Trace (HEAT) to tackle this problem. Our approach modulates on-the-fly the resolution of the interactive search in different parts of the image collection, by relying on a hierarchical organization of the images computed off-line. Internally, the strategy is to maintain an accurate approximation of the probabilities of relevance of the individual images while fixing an upper bound on the required computation. Our system is compared on the ImageNet database to the state-of-the-art approach it extends, by conducting user evaluations on a sub-collection of 33,000 images. Its scalability is then demonstrated by conducting similar evaluations on 1,000,000 images.