Multilevel Image Thresholding for Image Segmentation by Optimizing Fuzzy Entropy using Firefly Algorithm

Abstract

Image thresholding is the process of extracting objects in a scene from the background accompanies for the analysis and interpretation of image which is mostly employed for its advanced simplicity, robustness, less convergence time and accuracy. The main intend of image segmentation is to segregate the foreground from background. As ordinary thresholding method of image segmentation is computationally expensive while extending for multilevel image thresholding, the need for optimization techniques is highly recommended. The so called optimization techniques such as Particle swarm optimization and bat algorithm undergo instability when the particle velocity is maximum and stagnation stage attributable to quick exploration. This paper proposes for the first time the multilevel image thresholding for image segmentation by using Fuzzy entropy maximized by naturally inspired firefly algorithm. A firefly based multilevel image thresholding is established by maximizing Fuzzy entropy where the results are proved better in misclassification, standard deviation, Structural Similarity Index and segmented image quality while comparing with differential evolution, Particle swarm optimization and bat algorithm..