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The Application of Fuzzy Hopfield Neural Network to Design Better Codebook for Image Vector Quantization
JzauSheng LIN ShaoHan LIU ChiYuan LIN
Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Vol.E81A
No.8
pp.16451651 Publication Date: 1998/08/25 Online ISSN:
DOI: Print ISSN: 09168508 Type of Manuscript: Special Section PAPER (Special Section on Digital Signal Processing) Category: Keyword: vector quantization, Hopfield net, fuzzy Hopfield net,
Full Text: PDF(805.6KB)>>
Summary:
In this paper, the application of an unsupervised parallel approach called the Fuzzy Hopfield Neural Network (FHNN) for vector qunatization in image compression is proposed. The main purpose is to embed fuzzy reasoning strategy into neural networks so that online learning and parallel implementation for codebook design are feasible. The object is to cast a clustering problem as a minimization process where the criterion for the optimum vector qunatization is chosen as the minimization of the average distortion between training vectors. In order to generate feasible results, a fuzzy reasoning strategy is included in the Hopfield neural network to eliminate the need of finding weighting factors in the energy function that is formulated and based on a basic concept commonly used in pattern classification, called the "withinclass scatter matrix" principle. The suggested fuzzy reasoning strategy has been proven to allow the network to learn more effectively than the conventional Hopfield neural network. The FHNN based on the withinclass scatter matrix shows the promising results in comparison with the cmeans and fuzzy cmeans algorithms.

