Computer Engineering and Applications ›› 2009, Vol. 45 ›› Issue (31): 158-160.DOI: 10.3778/j.issn.1002-8331.2009.31.047

• 图形、图像、模式识别 • Previous Articles     Next Articles

Image segmentation algorithm based on Particle Swarm Optimization fuzzy C-means clustering

LI Li-li1,LI Ming2,LIU Xi-yu3   

  1. 1.School of Information Science and Engineering,Shandong Normal University,Jinan 250014,China
    2.Laboratory Management Office,Shandong Normal University,Jinan 250014,China
    3.School of Management and Economics,Shandong Normal University,Jinan 250014,China
  • Received:2008-06-18 Revised:2008-10-22 Online:2009-11-01 Published:2009-11-01
  • Contact: LI Li-li

基于粒子群模糊C-均值聚类的图像分割算法

李丽丽1,李 明2,刘希玉3   

  1. 1.山东师范大学 信息科学与工程学院,济南 250014
    2.山东师范大学 实验室管理处,济南 250014
    3.山东师范大学 管理与经济学院,济南 250014
  • 通讯作者: 李丽丽

Abstract: The Fuzzy C-Means(FCM) clustering algorithm is an effective image segmentation algorithm.But it is sensitive to initial clustering center and membership matrix and likely converges into the local minimum,which causes the quality of image segmentation lower.A new image segmentation algorithm is proposed,which combines the particle swarm optimization(PSO) and FCM clustering.Some experimental results are given,which show that the algorithm has the effective ability of searching global optimal solution.

Key words: image segmentation, Particle Swarm Optimization, Fuzzy C-mean clustering algorithm, global optimization

摘要: 模糊C-均值(FCM)聚类算法是一种结合无监督聚类和模糊集合概念的图像分割技术,比较有效,但存在着受初始聚类中心和隶属度矩阵影响,可能收敛到局部极小的缺点。将粒子群优化算法(PSO)与模糊C-均值聚类算法相结合,实现了基于粒子群模糊C-均值聚类的图像分割算法。实验表明,该方法具有搜索全局最优解的能力,因而可得到很好的图像分割结果。

关键词: 图像分割, 粒子群优化算法, 模糊C-均值聚类算法, 全局优化

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