An efficient clustering method for retrieval of large image databases

Abstract

This paper proposes a clustering method called CMA, which supports content-based retrieval of large image databases. CMA takes advantages of k-means and self-adaptive algorithms. It is simple and works without any user interactions. There are two main stages in this algorithm. In the first stage, it classifies images in a database into several clusters, and automatically gets the necessary parameters for the next stage - k-means iteration. We test our CMA algorithm on a large database of more than ten thousand images. Experiments show the effectiveness of this method. © Springer-Verlag 2004.