Maximum entropy clustering algorithms and their application in image compression

This paper presents a new approach to fuzzy clustering, which provides the basis for the development of maximum entropy clustering algorithms (MECA). The derivation of the proposed algorithms is based on an objective function incorporating the partition entropy and the average distortion between the prototypes and the feature vectors. This formulation allows the gradual transition from a maximum uncertainty or minimum selectivity phase to a minimum uncertainty or maximum selectivity phase during the clustering process. The application of the proposed algorithms in image compression based on vector quantization provides the basis for evaluating their computational efficiency and comparing the quality of the resulting codebook design with that provided by competing techniques