Combining feedback and image database categorization in CBIR

We propose a content-based image retrieval prototype that combines the advantages of relevance feedback and image database categorization. Our approach is based on an algorithm that performs clustering and feature weighting simultaneously and can incorporate partial supervision information. This information, extracted from the userpsilas feedback through visual exploration and interaction, is used to refine the clusterspsila distributions and their feature relevance weights in the vicinity of the query image. The cluster dependent feature weights are used in the retrieval phase to adapt the similarity to the different categories. The feedback information is encoded as a set of constraints on which instances should or should not reside in the same cluster. Thus, it does not depend on the query image explicitly. Consequently, partial supervision information from different query sessions could be saved, accumulated, and used to continuously refine the image categories and their feature weights.