کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
567239 | 876063 | 2007 | 12 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: 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](/preview/png/567239.png)
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.
Journal: Signal Processing - Volume 87, Issue 5, May 2007, Pages 799–810