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

研究成果: 書貢獻/報告類型會議論文篇章同行評審

3 引文 斯高帕斯(Scopus)

摘要

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.

原文???core.languages.en_GB???
主出版物標題2006 IEEE Intelligent Vehicles Symposium, IV 2006
頁面244-249
頁數6
出版狀態已出版 - 2006
事件2006 IEEE Intelligent Vehicles Symposium, IV 2006 - Meguro-Ku, Tokyo, Japan
持續時間: 13 6月 200615 6月 2006

出版系列

名字IEEE Intelligent Vehicles Symposium, Proceedings

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???event.eventtypes.event.conference???2006 IEEE Intelligent Vehicles Symposium, IV 2006
國家/地區Japan
城市Meguro-Ku, Tokyo
期間13/06/0615/06/06

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