Cluster-based relevance feedback for CBIR: a combination of query point movement and query expansion

Abstract

This paper presents a cluster-based relevance feedback method, which combines two popular techniques of relevance feedback: query point movement and query expansion. Inspired from text retrieval, these two techniques are giving good results for image retrieval. But query point movement is limited by a constraint of unimodality in taking into account the user feedbacks. Query expansion gives better results than query point movement, but it cannot take into account irrelevant images from the user feedbacks. We combine the two techniques to profit from their advantages and to cope with their limitations. From a single point initial query, query expansion provides a multiple point query, which is then enhanced using query point movement. To learn the multiple point queries, the irrelevant feedback images are classified into query points which are clustered from relevant images using the query expansion technique. The experiments show that our method gives better results in comparison with the two techniques of relevance feedback taken individually.