Computer Engineering and Applications ›› 2017, Vol. 53 ›› Issue (13): 189-195.DOI: 10.3778/j.issn.1002-8331.1509-0299

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Fast KFCM clustering segmentation algorithm with local information

HOU Xiaofan, WU Chengmao   

  1. School of Electronic Engineering, Xi’an University of Posts and Telecommunications University, Xi’an 710121, China
  • Online:2017-07-01 Published:2017-07-12



  1. 西安邮电大学 电子工程学院,西安 710121

Abstract: For Fuzzy C-Means clustering with local informationand kernel metric for image segmentation algorithm time complexity too large to fit in real-time occasion required, a fast nuclear space algorithm of fuzzy local information C-means clustering segmentation is proposed. Firstly, the paper uses the space distance between pixels neighborhood pixels with information and gray variance information to construct a weighted co-occurrence matrix. Secondly it combines one-dimensional histogram with two-dimensional histogram between the pixels and neighborhood pixels constructed objective function of the new algorithm. Thirdly, to improve the algorithm of noise immunity, filter processing is done to neighborhood pixels membership when pixel classification. The experimental results show that compared with KWFLICM, the proposed algorithm has advantages of better performance and save time.

Key words: image segmentation, Fuzzy C-Means clustering, histogram, weighted symbiotic matrix, neighborhood filtering

摘要: 针对核空间模糊局部C-均值聚类分割算法时间复杂性过大而不适合实时场合图像分割需要的问题,提出了一种核空间局部模糊C-均值聚类分割的快速算法。利用像素与其邻域像素之间的空间距离信息和灰度方差信息构造一种加权共生矩阵;将图像像素的一维直方图以及像素与邻域像素之间的二维共生直方图相结合构造了一种新的核空间模糊C-均值聚类分割目标函数,并对其推导获得隶属度和聚类中心迭代表达式;将图像像素采用该算法聚类所得隶属度进行邻域滤波处理,以便改善该算法的抗噪性能。实验结果表明,该分割算法相比核空间局部模糊C-均值聚类分割更有利于实时场合和大幅面图像分割的需要。

关键词: 图像分割, 模糊C-均值聚类, 直方图, 加权共生矩阵, 邻域滤波