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17 December 1998 Video and image clustering using relative entropy
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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.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Giridharan Iyengar and Andrew B. Lippman "Video and image clustering using relative entropy", Proc. SPIE 3656, Storage and Retrieval for Image and Video Databases VII, (17 December 1998);


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