Competitive neural networks as adaptive algorithms for nonstationary clustering: experimental results on the color quantization of image sequences

In this paper we consider the application of several architectures of competitive neural networks to the adaptive computation of cluster representatives (codevectors) over nonstationary data. Adaptive computation shifts the emphasis from robust global optimization to fast local optimization from good initial conditions. The paradigm of nonstationary clustering is represented by the problem of color quantization of image sequences. Experimental results applying the diverse architectures to the adaptive computation of color representatives for the color quantization of an image sequence are given and discussed