WestminsterResearch

Improved batch fuzzy learning vector quantization for image compression

Tsekouras, George E. and Antonios, Mamalis and Anagnostopoulos, Christos and Gavalas, Damianos and Economou, Daphne (2008) Improved batch fuzzy learning vector quantization for image compression. Information Sciences, 178 (20). pp. 3895-3907. ISSN 0020-0255

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Official URL: http://dx.doi.org/10.1016/j.ins.2008.05.017

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.

Item Type:Article
Research Community:University of Westminster > Electronics and Computer Science, School of
ID Code:7357
Deposited On:26 Jan 2010 10:29
Last Modified:26 Jan 2010 10:29

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