An adaptive hierarchical clustering approach for relevance feedback in content-based image retrieval systems

This paper proposes a new, fast approach for relevance feedback in content-based image retrieval systems. The main advantage of the proposed approach is the use of the set of primarily retrieved images instead of performing another query. The images are hierarchically clustered with respect to the positive/ negative examples provided by the user, in a continuous manner, as the user successively browses through new sets of retrieved images. The proposed aggregative hierarchical clustering relevance feedback embeds an automatic, adaptive stopping criterion. The paper further investigates the effect of the inter-cluster dissimilarity metric (minimum distance, maximum distance, centroid distance and medium distance) on the image retrieval performance for various image databases.