Centroid neural network adaptive resonance theory for vector quantization

Authors:
Tzu-Chao Lin;Pao-Ta Yu
Affiliations:
Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, 62107 Taiwan, ROC;Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, 62107 Taiwan, ROC
Venue:
Signal Processing
Year:
2003

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Abstract

In this paper, a novel unsupervised competitive learning algorithm, called the centroid neural network adaptive resonance theory (CNN-ART) algorithm, is proposed to relieve the dependence on the initial codewords of the codebook in contrast to the conventional algorithms with vector quantization in lossy image compression. The design of the CNN-ART algorithm is mainly based on the adaptive resonance theory structure, and then a gradient-descent-based learning rule is derived so that the CNN-ART algorithm does not require a predetermined schedule for learning rate. Furthermore, the appropriate initial weights obtained by the CNN-ART algorithm can be applied as an initial codebook for the Linde-Buzo-Gray (LBG) algorithm such that the compression performance can be greatly improved. In this paper, the extensive simulations demonstrate that the CNN-ART algorithm does outperform other algorithms like LBG, self-organizing feature map and differential competitive learning.