现代电子技术2018，Vol.41Issue(10)：164-168,5.DOI:10.16652/j.issn.1004⁃373x.2018.10.042
基于动态粒子群优化与K-means聚类的图像分割算法
Image segmentation algorithm based on dynamic particle swarm optimization and K-means clustering
摘要
Abstract
An image segmentation algorithm based on dynamic particle swarm optimization and K-means clustering(DP-SOK)is proposed to resolve the problems that the image segmentation quality of K-means clustering algorithm overly relies on the selection of initial clustering center,and it is easy for the algorithm to fall into the local optimal solution. The performance of the particle swarm optimization(PSO)algorithm is enhanced by dynamically adjusting the inertia coefficient and the learning factor. The variance of the particle swarm adaptability is calculated,and the timing of switching to the K-means algorithm is cap-tured. The output results of dynamic particle swarm optimization(DPSO)are used to initialize the K-means clustering center and enable it to converge to the global optimal solution. The K-means clustering center is updated constantly until reaching con-vergence by means of multiple iterations of the minimized objective function. The experimental results show that the DPSOK can effectively improve the global search capability of K-means,obtain a better segmentation effect than K-means and the PSO in im-age segmentation,and has higher segmentation quality and efficiency in comparison with the particle swarm optimization and K-means algorithm.关键词
图像分割/动态粒子群优化/K-means聚类/适应度方差/聚类算法/DPSOKKey words
image segmentation/dynamic particle swarm optimization/K-means clustering/fitness variance/clustering al-gorithm/DPSOK分类
信息技术与安全科学引用本文复制引用
李立军,张晓光..基于动态粒子群优化与K-means聚类的图像分割算法[J].现代电子技术,2018,41(10):164-168,5.基金项目
国家自然科学基金项目(51274202) （51274202）
教育部第六批国家特色专业建设项目(TS1Z293) （TS1Z293）
江苏省自然科学基金项目(BK20130199)Project Supported by National Nature Science Foundation of China(51274202),6th National Special Major Construction Project of Ministry of Education(TS1Z293),Nature Science Foundation of Jiangsu Province(BK20130199) （BK20130199）
