A fuzzy entropy based multi-level image thresholding using differential evolution

Abstract

This paper presents a multi-level image thresholding approach based on fuzzy partition of the image histogram and entropy theory. Here a fuzzy entropy based approach is adopted in context to the multi-level image segmentation scenario. This entropy measure is then optimized to obtain the thresholds of the image. In order to solve the optimization problem, a meta-heuristic, Differential Evolution (DE) is used, which leads to a faster and accurate convergence towards the optima. The performance of DE is also measured with respect to some popular global optimization techniques like Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs).The outcomes are compared with Shannon entropy, both visually and statistically in order to establish the perceptible difference in image.