基于快速模糊C均值聚类的图像粗集分割  

Image rough sets segmentation based on fast fuzzy C-means clustering

作　　者：李云松[1] 冯玉东[1] 张国锋[1] 

机构地区：[1]郑州电力高等专科学校电子信息系,河南郑州450004

出　　处：《兰州理工大学学报》2013年第1期92-96,共5页Journal of Lanzhou University of Technology

基　　金：河南省教育厅自然科学研究项目(2010C520016)

摘　　要：传统的模糊C均值(FCM)聚类算法广泛用于图像的自动分割,但它有两个缺陷:一是收敛速度过慢;二是当图像的目标和背景像素拥有相近的灰度值,具有相似的隶属度,导致了图像边界区域的不连续和模糊.针对该问题,提出一种改进的算法,在快速FCM聚类的基础上,利用粗糙集理论中的上近似和下近似的概念来描述图像的目标和背景,引入粗糙熵的概念,选择合适的阈值,对图像进行精确分割.实验结果表明,这种算法可以达到满意的分割效果.The conventional Fuzzy C-Means（FCM） clustering algorithm was widely used in automatic segmentation of image.However,there were two defects： one being slow converging speed;the other being that the boundary regions of image would become discontinuous and vague when the image＇s object pixels and background pixels had similar grey scale and membership grade.Therefore,a improved algorithm was proposed to eliminate them.On the basis of fast FCM clustering,the object and back ground of the image were described with the concepts of upper and lower approximation in the rough sets theory and a concept of rough entropy was introduced.Therefore,the image would be segmented accurately after a threshold value being chosen.

关 键 词：快速模糊C均值 粗糙集 粗糙熵 图像分割 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]