A new unsupervised competitive learning algorithm for vector quantization

In this paper, a novel unsupervised competitive learning algorithm, called the centroid neural network adaptive resonance theory (CNN-ART) algorithm, is to be 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 (ART) 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. The appropriate initial weights obtained from the CNN-ART algorithm can be applied as an initial codebook of 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 Re LBG, SOFM and DCL.