An automatic fuzzy c-means algorithm for image segmentation

被引:0
作者
Yan-ling Li
Yi Shen
机构
[1] Huazhong University of Science and Technology,Department of Control Science and Engineering, Institute of System Engineering
来源
Soft Computing | 2010年 / 14卷
关键词
Image segmentation; Fuzzy ; -means; Fuzzy clustering; -means; Spatial information;
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 for image segmentation. However, the standard FCM algorithm must be estimated by expertise users to determine the cluster number. So, we propose an automatic fuzzy clustering algorithm (AFCM) for automatically grouping the pixels of an image into different homogeneous regions when the number of clusters is not known beforehand. In order to get better segmentation quality, this paper presents an algorithm based on AFCM algorithm, called automatic modified fuzzy c-means cluster segmentation algorithm (AMFCM). AMFCM algorithm incorporates spatial information into the membership function for clustering. The spatial function is the weighted summation of the membership function in the neighborhood of each pixel under consideration. Experimental results show that AMFCM algorithm not only can spontaneously estimate the appropriate number of clusters but also can get better segmentation quality.
引用
收藏
页码:123 / 128
页数:5
相关论文
共 27 条
  • [1] Ball G(1967)A clustering technique for summarizing multivariate data Behav Sci 12 153-155
  • [2] Hall D(1974)Cluster validity with fuzzy sets J Cybern 3 58-73
  • [3] Bezdek JC(1974)A fuzzy relative of the ISODATA process and its use in detecting compact, well-separated clusters J Cybern 3 32-57
  • [4] Dunn JC(2007)Fuzzy ants and clustering IEEE Trans Syst Man Cybern A Syst Hum 37 758-769
  • [5] Hall LO(2006)Ant colony optimization algorithm based on directional pheromone diffusion Chin J Electron 15 447-450
  • [6] Kanade PM(2004)On cluster validity index for estimation of optimal number of fuzzy clusters Pattern Recognit 37 2009-2024
  • [7] Huang GR(1995)On cluster validity for the fuzzy c-means model IEEE Trans Fuzzy Syst 3 370-379
  • [8] Wang XF(1999)An adaptive fuzzy c-means algorithm for image segmentation in the presence of intensity inhomogeneities Pattern Recognit Lett 20 57-68
  • [9] Cao XB(1996)Fuzzy connectedness and object definition: theory, algorithm and applications in image segmentation Graph Models Image Process 58 246-261
  • [10] Kim DW(2004)An ant colony approach for clustering Anal Chim Acta 509 187-195