Title: Mutual-inclusive learning-based multi-swarm PSO algorithm for image segmentation using an innovative objective function
Authors: Rupak Chakraborty; Rama Sushil; Madan L. Garg
Addresses: Department of Computer Science and Engineering, Bennett University, Greater Noida, UP, India ' Department of Information Technology, DIT University, Dehradun, India ' Department of Computer Science and Engineering, DIT University, Dehradun, India
Abstract: This paper presents a novel image segmentation algorithm formed by the normalised index value (Niv) and probability (Pr) of pixel intensities. To reduce the computational complexity, a mutual-inclusive learning-based optimisation strategy, named mutual-inclusive multi-swarm particle swarm optimisation (MIMPSO) is also proposed. In mutual learning, a high dimensional problem of particle swarm optimisation (PSO) is divided into several one-dimensional problems to get rid of the 'high dimensionality' problem whereas premature convergence is removed by the inclusive-learning approach. The proposed Niv and Pr-based technique with the MIMPSO algorithm is applied on the Berkley Dataset (BSDS300) images which produce better optimal thresholds at a faster convergence rate with high functional values as compared to the considered optimisation techniques like PSO, genetic algorithm (GA) and artificial bee colony (ABC). The overall performance in terms of the fidelity parameters of the proposed algorithm is carried out over the other stated global optimisers.
Keywords: multilevel thresholding; normalised index value; probability; multi-swarm PSO.
International Journal of Computational Science and Engineering, 2020 Vol.21 No.4, pp.483 - 494
Received: 26 Sep 2018
Accepted: 18 Mar 2019
Published online: 24 Apr 2020 *