Video and image clustering using relative entropy

In this paper, we present an approach to clustering video sequences and images for efficient retrieval using relative entropy as our cost criterion. In addition, our experiments indicate that relative entropy is a good similarity measure for content-based retrieval. In our clustering work, we treat images and video as probability density functions over the extracted features. This leads us to formulate a general algorithm for clustering densities. In this context, it can be seen that a euclidean distance between features and the Kullback-Liebler (KL) divergence, give equivalent clustering. In addition, the asymmetry of the KL divergence leads to another clustering. Our experiments indicate that this clustering is more robust to noise and distortions, compared with the one resulting from euclidean norm.