Iterative fuzzy vector quantization and its neural net algorithm

Yong Hu; Zheng Tan

doi:10.1117/12.280634

22 July 1997 Iterative fuzzy vector quantization and its neural net algorithm

Yong Hu, Zheng Tan

Proceedings Volume 3074, Visual Information Processing VI; (1997) https://doi.org/10.1117/12.280634
Event: AeroSense '97, 1997, Orlando, FL, United States

Abstract

This paper presents an iterative fuzzy vector quantization approach used in codebook design and its neural net algorithm, the fuzzy self-organizing feature map (FSOFM) algorithm, which is the development of the self-organizing feature map (SOFM) algorithm and the fuzzy vector quantization (FVQ) algorithms. The FVQ algorithm allows that each training vector is assigned to multiple codewords in the early stages of the codebook design. ALthough, the FVQ algorithm reduces the dependence of the resulting codebook on the initial codebook, the codewords are calculated in batch mode. The iterative fuzzy vector quantization approach is based on a gradient decent approach, and the concept of fuzzy is introduced into it. The FSOFM algorithm considers the winning output node and its neighborhood as a fuzzy set of the input node. As a result, the feature vector of the output node in the fuzzy set of the input sample can be updated by the membership function and the training vector just completing once iteration. In this paper, the LBG, FVQ, SOFM and FSOFM algorithms are used in image compression based on vector quantization. This paper evaluates the computing efficiency of these algorithms and compares the quality of the resulting codebooks.

Citation Download Citation

Yong Hu and Zheng Tan "Iterative fuzzy vector quantization and its neural net algorithm", Proc. SPIE 3074, Visual Information Processing VI, (22 July 1997); https://doi.org/10.1117/12.280634

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