Improved fuzzy clustering algorithm with non-local information for image segmentation
作者: Xiaofeng ZhangYujuan SunGang WangQiang GuoCaiming ZhangBeijing Chen
作者单位: 1Ludong University
2Shandong Provincial Key Laboratory of Digital Media Technology
3Shandong University of Finance and Economics
4Nanjing University of Information Science & Technology
刊名: Multimedia Tools and Applications, 2017, Vol.76 (6), pp.7869-7895
来源数据库: Springer Nature Journal
DOI: 10.1007/s11042-016-3399-x
关键词: Fuzzy clusteringImage segmentationFLICMPixel relevanceNon-local information
原始语种摘要: Fuzzy C-means(FCM) has been adopted to perform image segmentation due to its simplicity and efficiency. Nevertheless it is sensitive to noise and other image artifacts because of not considering spatial information. Up to now, a series of improved FCM algorithms have been proposed, including fuzzy local information C-means clustering algorithm(FLICM). In FLICM, one fuzzy factor is introduced as a fuzzy local similarity measure, which can control the trade-off between noise and details. However, the fuzzy factor in FLICM cannot estimate the damping extent of neighboring pixels accurately, which will result in poor performance in images of high-level noise. Aiming at solving this problem, this paper proposes an improved fuzzy clustering algorithm, which introduces pixel relevance into the...
全文获取路径: Springer Nature  (合作)
影响因子:1.014 (2012)

  • segmentation 分段
  • clustering 聚类
  • fuzzy 模糊的
  • image 
  • algorithm 算法
  • local 局部的
  • information 报告
  • FCM File Compare Mask
  • improved 改进
  • simplicity 简单