An efficient memorization scheme for relevance feedback in image retrieval

We propose a novel approach to memorizing content relevance information accumulated in relevance feedback sessions (historical relevance feedback) in image retrieval. It is based on the idea that by storing identification numbers of several representative images for each positive image after a query session, it is possible to provide more precise and diverse retrieval results when this image is used later as a query in a new search session. To do so, a criterion for a "good" buddy image is proposed. Based on such criterion, an algorithm is designed to maximize the space spanned by the selected buddy images. Since the proposed approach requires only a constant storage space for each image, it has good scalability for a large size of image database. Experimental results on a database of 10,000 images show the high efficiency and good scalability of the proposed memorization scheme.