Multi-level fractional order PSO new paradigm algorithm for image segmentation

Abstract

Nowadays, the resolution of the image characterisation problem is a very active and developed research field. Various applications areas were processed such as robotics and autonomous systems, computer vision, map processing and medical imaging. Pre-processing, segmentation, recognition and classification are the main areas of study. Multilevel segmentation of Benchmark images is our application field in this study. It is used to separate the original image into regions with common characteristics of an interesting viewing quality and a fast time execution. The purpose is to automatically determine the optimal threshold values based between-class variance maximisation. The choice of the already used method essentially depends on the quantitative characteristics. We note the optimal threshold values, the minimum CPU processing time and the stability of the proposed fitness function. Likewise, qualitatively results are required. The principle aim of this paper is to propose a new PSO algorithm for multilevel segmentation based on a novel fitness function and modified inertia component to find the optimal thresholds. Experimental results applied on a set of benchmarks images have been proven efficiencies and advantages in multilevel compared to other metaheuristics such as Genetic Algorithms (GA), Otsu method, Conventional PSO and Fractional-Order Darwinian PSO (DPSO).