A novel approach for vector quantization using a neural network, mean shift, and principal component analysis-based seed re-initialization

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Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/31765

Title:	A novel approach for vector quantization using a neural network, mean shift, and principal component analysis-based seed re-initialization
Authors:	Han,Chin-Chuan;Chen,Ying-Nong;Lo,Chih-Chung;Wang,Cheng-Tzu
Contributors:	資訊工程研究所
Keywords:	ALGORITHM
Date:	2007
Issue Date:	2010-07-06 18:11:07 (UTC+8)
Publisher:	中央大學
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 Linde-BLIzo-Gray (LBG) appro
Relation:	SIGNAL PROCESSING
Appears in Collections:	[Graduate Institute of Computer Science and Information Engineering] journal & Dissertation

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