Open Access

ARTICLE

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

Zhe Liu^1,2,*, Bao Xiang^1,3, Yuqing Song¹, Hu Lu¹, Qingfeng Liu¹

1 School of Computer Science and Telecommunication Engineering, Jiangsu University, Zhenjiang, 212013, China.
2 School of Computer Science, Jilin Normal University, Siping, 136000, China.
3 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.

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Citations