A spread neural network with fuzzy clustering technique applied to color image coding in the MDT domain

Chi Yuan Lin, Chin-Hsing Chen, Ruey Maw Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this paper an unsupervised parallel approach called fuzzy competitive learning network (FCLN) for vector quantization (VQ) and spread FCLN (SFCLN) for color image compression in the mean value/difference value transform (MDT) domain are proposed. In the FCLN, the codebook design is conceptually considered as a clustering problem. Here, it is a kind of competitive learning network model imposed by the fuzzy clustering strategies working toward minimizing an objective function defined as the average distortion measure between any two training vectors within the same class. The color image information transformed by the MDT operation was separated into RGB 3-plane mean value and detail coefficients. Then the detail coefficients for each plane were trained using the proposed SFCLN method to generate the VQ codebook. The experimental results show that promising codebooks can be obtained using the proposed FCLN and SFCLN for color image compression in the MDT domain.

Original languageEnglish
Title of host publicationProceedings - 9th International Conference on Parallel and Distributed Systems, ICPADS 2002
PublisherIEEE Computer Society
Pages583-588
Number of pages6
ISBN (Electronic)0769517609
DOIs
Publication statusPublished - 2002 Jan 1
Event9th International Conference on Parallel and Distributed Systems, ICPADS 2002 - Taiwan, China
Duration: 2002 Dec 172002 Dec 20

Publication series

NameProceedings of the International Conference on Parallel and Distributed Systems - ICPADS
Volume2002-January
ISSN (Print)1521-9097

Other

Other9th International Conference on Parallel and Distributed Systems, ICPADS 2002
Country/TerritoryChina
CityTaiwan
Period02-12-1702-12-20

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture

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