Human Perception-Based Color Image Segmentation Using Comprehensive Learning Particle Swarm Optimization

Abstract

In computer vision, image processing is any form of signal processing for which the input is an image, such as photographs or frames of videos. The output of image processing can be either an image or a set of characteristics or parameters related to image. The color vision systems require a first step of classifying pixels in a given image into a discrete set of color classes. The aim is to produce a fuzzy system for color classification and image segmentation with least number of rules and minimum error rate. Fuzzy sets are defined on the H, S and L components of the HSL color. Particle swarm optimization is a sub class of evolutionary algorithms that has been inspired from social behavior of fishes, bees, birds, etc, that live together in colonies. During evolution, a population member tries to maximize a fitness criterion which is here high classification rate and small number of rules. Finally, particle with the highest fitness value is selected as the best set of fuzzy rules for image segmentation. In Comprehensive learning particle Swarm optimization specific weight is assigned to each color for obtaining high classification rate.