A comparison of 1-D and 2-D self-organizing feature map algorithm on color image quantization

Abstract

Color quantization process is performed by clustering in color space. The clustering algorithm we examine is self-organizing feature map (SOFM) introduced by Kohonen. In this application we use a one- and two-dimensional self-organizing neural network and compare them. In the competitive learning process, the weigh vectors for each neuron are produced to represent each cluster and each color in the image is placed in the closest cluster. Our application supports mapping from 256-color to 16-color images to show the quantization results.