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

Authors: Fayçal Hamdaoui; Anis Ladgham; Anis Sakly; Abdellatif Mtibaa

Addresses: Laboratory of EμE, Faculty of Sciences of Monastir, Electrical Department, National Engineering School of Monastir (ENIM), University of Monastir, Av Ibn ElJazzar 5019, Monastir, Tunisia ' Laboratory of EμE, Faculty of Sciences of Monastir, Electrical Department, National Engineering School of Monastir (ENIM), University of Monastir, Av Ibn ElJazzar 5019, Monastir, Tunisia ' Industrial Systems Study and Renewable Energy (ESIER), National Engineering School of Monastir (ENIM), Electrical Department, University of Monastir, Av Ibn ElJazzar 5019, Monastir, Tunisia ' Laboratory of EμE, Faculty of Sciences of Monastir, Electrical Department, National Engineering School of Monastir (ENIM), University of Monastir, Av Ibn ElJazzar 5019, Monastir, Tunisia

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).

Keywords: image segmentation; multilevel thresholding; particle swarm optimisation; novel fitness function; fractional-order PSO; modified inertia; metaheuristics.

DOI: 10.1504/IJSISE.2016.078261

International Journal of Signal and Imaging Systems Engineering, 2016 Vol.9 No.4/5, pp.218 - 225

Received: 23 May 2015
Accepted: 08 Feb 2016
Published online: 10 Aug 2016 *

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