Focused Image Color Quantization using Magnitude Sensitive Competitive Learning Algorithm

Abstract

This paper introduces the Magnitude Sensitive Competitive Learning (MSCL) algorithm for Color Quantization. MSCL is a neural competitive learning algorithm, including a magnitude function as a factor of the measure used for the neuron competition. This algorithm has the property of distributing color vector prototypes in certain data-distribution zones according to an arbitrary magnitude locally calculated for every unit. Therefore, it opens the possibility not only to distribute the codewords (colors of the palette) according to their frequency, but also to do it in function of any other data-dependent magnitude focused on a given task. This work shows some examples of focused Color Quantization where the objective is to represent with high detail certain regions of interest in the image (salient area, center of the image, etc.). The oriented behavior of MSCL permits to surpass other standard Color Quantization algorithms in these tasks.