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

Author(s): Chen, Y.N. | Han, C.C. | Lo, C.C. | Wang, C.T. |

Year: 2007

Citation: SIGNAL PROCESSING Volume: 87 Issue: 5 Pages: 799-810

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) 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 lowutilized 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.

Topics: Machine Learning, Models: ART 1,

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Cross References

  1. The ART of adaptive pattern recognition by a self organizing neural network
    The adaptive resonance theory (ART) suggests a solution to the stability-plasticity dilemma facing designers of learning systems, namely how to design a learning system that will remain plastic, or adaptive, in response to ... Article Details