A ranking algorithm using dynamic clustering for content-based image retrieval

Abstract

In this paper, we propose a ranking algorithm using dynamic clustering for content-based image retrieval(CBIR). In conventional CBIR systems, it is often observed that visually dissimilar images to the query image are located at high ranking. To remedy this problem, we utilize similarity relationship of retrieved results via dynamic clustering. In the first step of our method, images axe retrieved using visual feature such as color histogram, etc. Next, the retrieved images are analyzed using a HACM (Hierarchical Agglomerative Clustering Method) and the ranking of results is adjusted according to distance from a cluster representative to a query. We show the experimental results based on MPEG-7 color test images. According to our experiments, the proposed method achieves more than 10 % improvements of retrieval effectiveness in ANMRR(Average Normalized Modified Retrieval Rank) performance measure.