A novel approach for VQ using a neural network, mean shift, and principal component analysis

Chin Chuan Han, Ying Nong Chen, Chin Chung Lo, Cheng Tzu Wang, Kuo Chin Fan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

In this paper, a hybrid approach for vector quantization(VQ) is proposed for obtaining the better codebook. It is modified and improved based on the centroid neural network, adaptive resonance theory (CNN-ART) and the enhanced LBG (Linde-Buzo-Gray) approaches. Three modules, a neuronal net (NN) based clustering, a mean shift (MS) based refinement, and a principal component analysis (PCA) based seed assignment, are repeatedly utilized. Basically, the seed assignment module generates a new initial codebook to replace the low utilized codewords during the iteration. The NN-based clustering module clusters the training vectors using a competitive learning approach. The clustered results are refined using the mean shift operation. Some experiments in image compression applications were conducted to show the effectiveness of the proposed approach.

Original languageEnglish
Title of host publication2006 IEEE Intelligent Vehicles Symposium, IV 2006
Pages244-249
Number of pages6
StatePublished - 2006
Event2006 IEEE Intelligent Vehicles Symposium, IV 2006 - Meguro-Ku, Tokyo, Japan
Duration: 13 Jun 200615 Jun 2006

Publication series

NameIEEE Intelligent Vehicles Symposium, Proceedings

Conference

Conference2006 IEEE Intelligent Vehicles Symposium, IV 2006
Country/TerritoryJapan
CityMeguro-Ku, Tokyo
Period13/06/0615/06/06

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