Comparison of different metaheuristic algorithms for multilevel non-local means 2d histogram thresholding segmentation

Abstract

Multilevel image segmentation technique segregates an image into disjoint regions and has application in many real-world problems like object recognition, boundary estimation of motion systems, image compression, etc. Conventional image segmentation does not consider the spatial correlation of image’s pixels and lack in providing better post-filtering efficiency. This paper performs an analysis of results obtained from different metaheuristic algorithms using an efficient technique of 2D histogram multilevel thresholding based on non-local means filter and Renyi entropy. Further, this study aims to compare newly proposed whale optimization algorithm with some prominent algorithms in recent past and some conventional metaheuristic algorithms to achieve an efficient image segmentation.