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 are 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.