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An Improved Unsupervised Image Segmentation Method Based on Multi-Objective Particle Swarm Optimization Clustering Algorithm

Zhe Liu1,2,*, Bao Xiang1,3, Yuqing Song1, Hu Lu1, Qingfeng Liu1
School of Computer Science and Telecommunication Engineering, Jiangsu University, Zhenjiang, 212013, China.
School of Computer Science, Jilin Normal University, Siping, 136000, China.
Institute of Science and Technology Information, Jiangsu University, Zhenjiang, 212013, China.
* Corresponding Author: Zhe Liu. Email: .

Computers, Materials & Continua 2019, 58(2), 451-461. https://doi.org/10.32604/cmc.2019.04069

Abstract

Most image segmentation methods based on clustering algorithms use single-objective function to implement image segmentation. To avoid the defect, this paper proposes a new image segmentation method based on a multi-objective particle swarm optimization (PSO) clustering algorithm. This unsupervised algorithm not only offers a new similarity computing approach based on electromagnetic forces, but also obtains the proper number of clusters which is determined by scale-space theory. It is experimentally demonstrated that the applicability and effectiveness of the proposed multi-objective PSO clustering algorithm.

Keywords

Multi-objective optimization, particle swarm optimization, electromagnetic forces, scale-space theory.

Cite This Article

Z. Liu, B. Xiang, Y. Song, H. Lu and Q. Liu, "An improved unsupervised image segmentation method based on multi-objective particle swarm optimization clustering algorithm," Computers, Materials & Continua, vol. 58, no.2, pp. 451–461, 2019.

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This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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