Neural network design
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vector quantization based on genetic simulated annealing
Signal Processing
Neural Networks
Using an Enhanced LBG Algorithm to Reduce the Codebook Error in Vector Quantization
CGI '00 Proceedings of the International Conference on Computer Graphics
An efficient prediction algorithm for image vector quantization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Image Processing
Image vector quantization algorithm via honey bee mating optimization
Expert Systems with Applications: An International Journal
Vector quantization using the firefly algorithm for image compression
Expert Systems with Applications: An International Journal
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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.