Distributional clustering for efficient content-based retrieval of images and video

We present an approach to clustering images for efficient retrieval using relative entropy. We start with the assumption that visual features are represented by probability densities and develop clustering algorithms for probability densities (for example, normalized histograms are crude approximations of probability densities). These clustering algorithms are then used for efficient retrieval of images and video