Improved FCM Clustering Algorithm Based on Spatial Correlation and Membership Smoothing

被引：13
|
作者：
Xiao M. [1 ,2 ]
Xiao Z. [1 ]
Wen Z. [2 ]
Zhou L. [2 ]
机构：
[1] College of Science and Technology, Hunan University of Technology, Zhuzhou
[2] School of Computer Science, Hunan University of Technology, Zhuzhou
来源：
Xiao, Mansheng (xiaomansheng@tom.com) | 1600年 / Science Press卷 / 39期
关键词：
Control parameter; Fuzzy C-Means (FCM); Membership smoothing; Spatial correlation; Spatial distance;
D O I：
10.11999/JEIT160710
中图分类号：
学科分类号：
摘要：
Concerning the problem that general Fuzzy C-Means (FCM) and its improved algorithm are sensitive to noise in the samples clustering and clustering boundary is not accurate enough, an improved FCM clustering algorithm based on spatial correlation is proposed. Firstly, it can improve the method of clustering center calculation and the function of distance calculation, through analyzing spatial distribution characteristics, interaction and influence value of the samples. Then, it redefines the fuzzy membership matrix through introducing a control parameter during summing membership of the samples with neighborhood information, thus realizing smoothing membership of neighborhood samples. Theoretical analysis and experimental results show that the improved algorithm has a better effect for samples with a lot of noise, and that the regional boundary value can process the image better. © 2017, Science Press. All right reserved.
引用
收藏
页码：1123 / 1129
页数：6
相关论文
共 17 条
  • [1] He L.H., Wen Y., Wan M., Et al., Multi-channel features based automated segmentation of diffusion tensor imaging using an improved FCM with spatial constraints, Neurocomputing, 137, pp. 107-114, (2014)
  • [2] Xiao M., Wen Z., Zhang J., Et al., An FCM clustering algorithm with improved membership function, Control and Decision, 30, 12, pp. 2270-2274, (2015)
  • [3] Shen X., He Y., Zhang B., Et al., FCM with spatial information and membership constrains for image segmentation, Journal of Beijing University of Technology, 38, 7, pp. 1073-1078, (2012)
  • [4] Yang Z., Feature extraction based on neighborhood structure and its applications to face recognition, (2014)
  • [5] Zhong C., Liu Z., Yang Y., Et al., Improved FCM algorithm based on neighboring membership constraint for image segmentation, Journal of Jilin University (Information Science Edition), 31, 6, pp. 627-633, (2013)
  • [6] Zhou W., Sun T., Zhu H., Image segmentation algorithm based on FCM optimized by adaptive spatial information, Application Research of Computers, 32, 7, pp. 2205-2208, (2015)
  • [7] Wang Y., The study of Fuzzy C-means algorithm incorporating spatial information for brain MR image segmentation, (2012)
  • [8] Kannan S.R., Devi R., Ramathilagam S., Et al., Effective FCM noise clustering algorithms in medical images, Computers in Biology and Medicine, 43, 2, pp. 73-83, (2013)
  • [9] Zhu C.J., Yang S.Z., Zhao Q., Et al., Robust semi-supervised kernel-FCM algorithm incorporating local spatial information for remote sensing image classification, Journal of the Indian Society of Remote Sensing, 42, 1, pp. 35-49, (2014)
  • [10] Qamar U., A dissimilarity measure based fuzzy c-means (FCM) clustering algorithm, Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology, 26, 1, pp. 229-238, (2014)
12