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

• 作者: Bao Xiang Hu Lu Qingfeng Liu Yuqing Song Zhe Liu
• 来源期刊: 计算机、材料和连续体(英文)
• 学科: 工学
• 关键词: MULTI-OBJECTIVE OPTIMIZATION particle SWARM OPTIMIZATION electromagnetic FORCES SCALE-SPACE theory
• 年，卷（期）: 2019,（2）
• 页码范围: 451-461
• 页数: 11页
• 分类号: TP3

摘要

Most image segmentation methods based on clustering algorithms use singleobjective 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.