Clusters-Based Relevance Feedback for CBIR: A Combination of Query Movement and Query Expansion

This paper presents a cluster-based relevance feedback technique. Our method combines two popular techniques of relevance feedback: query point movement and query expansion. The query expansion gives better result than the query point movement but there is still a problem with the insufficiency and the overlap of relevant and irrelevant images. To enhance the efficiency in such cases, irrelevant images are used to modify the multiple point queries of the query expansion technique by using the query point movement technique. To learn the multiple point queries, the irrelevant images are classified into query points which are clustered from relevant images in the query expansion technique. Inspired from text retrieval, query point movement and query expansion has given good result for image retrieval. In this paper, another technique inspired from text retrieval - the state-of-the-art Bag of Words model is used. The experiments show that our method gives better results in comparison with traditional methods of relevance feedback.