Centroid neural network adaptive resonance theory for vector quantization

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.