Cluster and Clustering Algorithm Validity in Image Retrieval

Abstract

To improve query efficiency, most image retrieval systems utilize clustering algorithms to build indices on image databases. In this paper, we analysis region-based image retrieval (RBIR) system query results, hypothesis test and Hubert's Gamma statistic for cluster validation. Our experiment results suggest that images originated from different categories do form clusters in the feature space and thus can be separated. We then analysis the performance of clustering algorithms including K-means, fuzzy C-mean, CA-clustering, density based spatial clustering of applications with noise (DBSCAN) and the proposed modified DBSCAN algorithm with a second merging phase. Our experiment results suggest that the proposed algorithm has clustering performance among the best algorithms. And the proposed modified DBSCAN algorithm with a second merging phase can avoid some of the drawbacks (e.g., k random initial cluster centers) in the original K-means algorithms.