Vector quantization of images based upon the Kohonen self-organizing feature maps
A neural-network clustering algorithm proposed by T. Kohonen (1986, 88) is used to design a codebook for the vector quantization of images. This neural-network clustering algorithm, which is better known as the Kohonen self-organizing feature maps, is a two-dimensional set of extensively interconnected nodes or unit of processors. The synaptic strengths between the input and the output nodes represent the centroid of the clusters after the network has been adapted to the input patterns. Input vectors are presented one at a time, and the weights connecting the input signals to the neurons are adaptively updated such that the point density function of the weights tends to approximate the probability density function of the input vector. Results are presented for a number of coded images using the codebook designed by the self-organization feature maps. The results are compared with coded images when the cookbook is designed by the Linde-Buzo-Gray algorithm. >