[1]毛晓波,张勇杰,陈铁军.基于蚁群及空间邻域信息的FCM图像分割方法[J].郑州大学学报(工学版),2014,35(01):1-4.[doi:10.3969/j.issn.1671-6833.2014.01.001]
　MAO Xiaobo,ZHANG Yongjie,CHEN Tiejun.Image Segmentation Based on the Ant Colony and Improved FCMClustering Algorithm with Spatial Information[J].Journal of Zhengzhou University (Engineering Science),2014,35(01):1-4.[doi:10.3969/j.issn.1671-6833.2014.01.001]
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基于蚁群及空间邻域信息的FCM图像分割方法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
35
期数:
2014年01期
页码:
1-4
栏目:
出版日期:
2014-02-28

文章信息/Info

Title:
Image Segmentation Based on the Ant Colony and Improved FCMClustering Algorithm with Spatial Information
作者:
毛晓波; 张勇杰; 陈铁军
郑州大学电气工程学院,河南郑州,450001
Author(s):
MAO Xiaobo;  ZHANG Yongjie;  CHEN Tiejun
School of Electrical Engineering, Zhengzhou University , Zhengzhou 450001, China
关键词:
Keywords:
ant colony algorithm;  watershed;  spalial information ;  image segmentation
DOI:
10.3969/j.issn.1671-6833.2014.01.001
文献标志码:
A
摘要:
针对模糊C均值(FCM)聚类算法聚类个数难以确定、搜索过程易陷入局部最优的缺陷,把蚁群算法与改进的FCM聚类算法相结合,提出了一种基于蚁群算法的带有空间邻域信息的模糊C均值聚类图像分割算法.首先利用分水岭算法对图像进行初始分割,然后利用蚁群算法寻优,求得聚类中心和聚类个数,将其作为模糊C均值聚类的初始聚类中心和聚类个数进行模糊聚类.实验结果表明:由于聚类样本数量显著减少,很大程度上提高了聚类速度和抗噪能力,增强了算法的鲁棒性.
Abstract:
With the fuzzy C-means clustering (FCM ) algorithm it is diffieult to determine the number of clus.ters on image segmentation, which is easy to get into a local oplimum. In order to solve the problems, this pa-per proposed a new segmentation method based on the ant eolony and improved FCM Clustering Algorithm withspatial information. Dividing image with the help of watershed algorithm, we got the initial segmentation re.sults. lt made full use of the ability of global optimization of the ant eolony algorithm to obtain the aeeurate o-riginal cluster centers and eluster number. Then the results were obtained as the iniial cluster eenters and thenumber of clusters of fuzzy C-means clustering algorihm. The experimental results show that: due to the de.erease of the size of clustering samples, the clustering speed, noise immunity and the robustness of the algo.rithm are improved signifcantly.
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