A novel approach for vector quantization using a neural network, mean shift, and principal component analysis-based seed re-initialization
A novel approach for vector quantization using a neural network, mean shift, and principal component analysis-based seed re-initialization
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DOI：
10.1016/j.sigpro.2006.08.006
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发表时间：
2007-05
期刊：
影响因子：
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通讯作者：
Chin-Chuan Han;Ying-Nong Chen;Chih-Chung Lo;Cheng-Tzu Wang
中科院分区：
文献类型：
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作者：
Chin-Chuan Han;Ying-Nong Chen;Chih-Chung Lo;Cheng-Tzu Wang
本文提出了一种矢量量化（VQ）的混合方法，以获得更好的码书。基于质心神经网络自适应共振理论（CNN-ART）和改进的Linde-Buzo-Gray（LBG）方法对该方法进行了改进和完善，以获得最优解。三个模块，神经网络（NN）为基础的聚类，均值漂移（MS）为基础的细化，和主成分分析（PCA）为基础的种子重新初始化，在这项研究中反复使用。基本上，种子重新初始化模块生成新的初始码本以在迭代期间替换低利用率码字。基于NN的聚类模块使用竞争学习方法对训练向量进行聚类。聚类结果使用均值漂移操作进行细化。在图像压缩应用中的实验结果表明了该方法的有效性。
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–Buzo–Gray (LBG) approaches to obtain the optimal solution. Three modules, a neural net (NN)-based clustering, a mean shift (MS)-based refinement, and a principal component analysis (PCA)-based seed re-initialization, are repeatedly utilized in this study. Basically, the seed re-initialization 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.