Improved batch fuzzy learning vector quantization for image compression

Authors:
George E. Tsekouras;Mamalis Antonios;Christos Anagnostopoulos;Damianos Gavalas;Dafne Economou
Affiliations:
University of the Aegean, Department of Cultural Technology and Communication, Laboratory of Intelligent Multimedia, Faonos & Harilaou Trikoupi Street, 81100 Mytilene, Greece;University of the Aegean, Department of Cultural Technology and Communication, Laboratory of Intelligent Multimedia, Faonos & Harilaou Trikoupi Street, 81100 Mytilene, Greece;University of the Aegean, Department of Cultural Technology and Communication, Laboratory of Intelligent Multimedia, Faonos & Harilaou Trikoupi Street, 81100 Mytilene, Greece;University of the Aegean, Department of Cultural Technology and Communication, Laboratory of Intelligent Multimedia, Faonos & Harilaou Trikoupi Street, 81100 Mytilene, Greece;University of the Aegean, Department of Cultural Technology and Communication, Laboratory of Intelligent Multimedia, Faonos & Harilaou Trikoupi Street, 81100 Mytilene, Greece
Venue:
Information Sciences: an International Journal
Year:
2008

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Abstract

In this paper, we develop a batch fuzzy learning vector quantization algorithm that attempts to solve certain problems related to the implementation of fuzzy clustering in image compression. The algorithm's structure encompasses two basic components. First, a modified objective function of the fuzzy c-means method is reformulated and then is minimized by means of an iterative gradient-descent procedure. Second, the overall training procedure is equipped with a systematic strategy for the transition from fuzzy mode, where each training vector is assigned to more than one codebook vectors, to crisp mode, where each training vector is assigned to only one codebook vector. The algorithm is fast and easy to implement. Finally, the simulation results show that the method is efficient and appears to be insensitive to the selection of the fuzziness parameter.