摘要
针对现有模糊C均值聚类(FCM)算法易出现过分割现象,分割效果不够理想等问题,本文提出了一种基于区域合并的FCM改进算法.该算法首先使用快速广义模糊C均值聚类算法(FGFCM)获得初始分割;然后综合考虑各区域间的邻接关系、颜色差异和边缘信息,计算各邻接区域间的距离;最后依据区域间距离和区域面积对初始分割区域进行合并,得到最终分割结果.实验证明,所提出的算法有更好的分割性能,有效解决了现有FCM分割算法中的过分割问题.
Aiming at the problems of over-segmentation and unsatisfactory segmentation in the existing fuzzy C-means clustering （FCM） algorithms，an improved FCM algorithm based on region merging is proposed in this paper.Firstly，the initial segmentation is obtained by the fast-generalized fuzzy C-means clustering algorithm （FGFCM）.Then，the distance between adjacent regions is calculated by taking into account adjacency relation，color difference and edge information.Finally，the final segmentation result is obtained by merging the initial segmentation region based on the distance and area.Experiments show that the proposed algorithm not only has better segmentation performance，but also effectively solves the problem of oversegmentation in existing FCM segmentation algorithm.
作者
胡学刚
段瑶
严思奇
HU Xue-gang;DUAN Yao;YAN Si-qi(College of Communication and Information Technology,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)
出处
《小型微型计算机系统》
CSCD
北大核心
2018年第9期2077-2080,共4页
Journal of Chinese Computer Systems
基金
国家自然科学基金项目(61571071)资助
重庆市自然科学基金重点项目(cstc2017jcyj XB0037)资助
关键词
图像分割
模糊C均值
邻接区域
区域合并
image segmentation
fuzzy C-means
adjacency region
region merging
