PSO based Fuzzy C-Means algorithm for image segmentation

被引：0
|
作者：
Li, Yanling [1 ,2 ]
Shen, Yi [1 ]
机构：
[1] Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
[2] Department of Computer Science, Xinyang Normal University, Xinyang 464000, China
来源：
Journal of Computational Information Systems | 2008年 / 4卷 / 05期
关键词：
Copying - Particle swarm optimization (PSO) - Fuzzy clustering - Clustering algorithms - Stochastic systems;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Fuzzy C-Means (FCM) algorithm is one of the most popular methods of image segmentation, but it is in essence a technology of searching local optimal solution. The algorithm's initial clustering centers are the stochastic selection which causes it to depend on the selection of the initial cluster centers excessively. FCM algorithm always converges at the local optimum and is sensitive to noise. In order to overcome those defects, this article proposes a PSO based Fuzzy C-Means algorithm. First the overall robustness advantages of the particle swarm algorithm are used to get the cluster centers of image. Then the results are obtained as the initial cluster centers of Fuzzy C-Means algorithm. The experimental results show that new algorithm can converge more quickly than the standard FCM algorithm and suppress the noise effectively. © 2008 Binary Information Press.
引用
收藏
页码：1885 / 1890
相关论文
共 50 条
  • [1] An Image Segmentation Algorithm Based On Fuzzy C-Means Clustering
    Zhang Xinbo
    Jiang Li
    [J]. PROCEEDINGS OF 2009 CONFERENCE ON COMMUNICATION FACULTY, 2009, : 123 - 126
  • [2] An Image Segmentation Algorithm Based on Fuzzy C-Means Clustering
    Zhang, Xin-bo
    Jiang, Li
    [J]. ICDIP 2009: INTERNATIONAL CONFERENCE ON DIGITAL IMAGE PROCESSING, PROCEEDINGS, 2009, : 22 - 26
  • [3] Medical Image Segmentation With Fuzzy C-Means and Kernelized Fuzzy C-Means Hybridized on PSO and QPSO
    Venkatesan, Anusuya
    Parthiban, Latha
    [J]. INTERNATIONAL ARAB JOURNAL OF INFORMATION TECHNOLOGY, 2017, 14 (01) : 53 - 59
  • [4] An automatic fuzzy c-means algorithm for image segmentation
    Yan-ling Li
    Yi Shen
    [J]. Soft Computing, 2010, 14 : 123 - 128
  • [5] An automatic fuzzy c-means algorithm for image segmentation
    Li, Yan-ling
    Shen, Yi
    [J]. SOFT COMPUTING, 2010, 14 (02) : 123 - 128
  • [6] Pythagorean fuzzy C-means algorithm for image segmentation
    Ma, Rong
    Zeng, Wenyi
    Song, Guangcheng
    Yin, Qian
    Xu, Zeshui
    [J]. INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, 2021, 36 (03) : 1223 - 1243
  • [7] A mean shift based fuzzy c-means algorithm for image segmentation
    Zhou, Huiyu
    Schaefer, Gerald
    Shi, Chunmei
    [J]. 2008 30TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-8, 2008, : 3091 - +
  • [8] Image Segmentation Algorithm Based on Context Fuzzy C-Means Clustering
    Xu Jindong
    Zhao Tianyu
    Feng Guozheng
    Ou Shifeng
    [J]. JOURNAL OF ELECTRONICS & INFORMATION TECHNOLOGY, 2021, 43 (07) : 2079 - 2086
  • [9] Oil Spill Image Segmentation Based on Fuzzy C-means Algorithm
    Sun Guangmin
    Ma Haocong
    Zhao Dequn
    Zhang Fan
    Jia Linan
    Sun Junling
    [J]. PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND INTELLIGENT COMMUNICATION, 2015, 16 : 406 - 409
  • [10] A fast underwater optical image segmentation algorithm based on a histogram weighted fuzzy C-means improved by PSO
    Wang S.
    Xu Y.
    Pang Y.
    [J]. Journal of Marine Science and Application, 2011, 10 (1) : 70 - 75
12345