Histogram Based Color Reduction through Self-Organized Neural Networks

Abstract

A new technique suitable for reduction of the number of colors in an image is presented in this paper. It is based on histogram processing and the use of Kohonen Self Organizing Feature Map (SOFM) neural networks. Initially, the dominant colors of each primary image are extracted through a simple linear piece-wise histogram approximation process. Then, using a SOFM the dominant color components of each primary color band are obtained and a look up table is constructed containing all possible color triplets. The final dominant colors are extracted from the look-up table entries using a SOFM by specifying the number of output neurons equal to the number of the dominant colors. Thus, the final image has all the dominant color classes. Experimental and comparative results demonstrate the applicability of the proposed technique.