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红外图像分割的改进模糊C均值聚类方法
Alternative TitleEnhanced Fuzzy C-Means clustering method for infrared image segmentation
魏英姿; 李静静
Department机器人学研究室
Source Publication计算机工程与应用
ISSN1002-8331
2014
Volume50Issue:9Pages:163-166
Indexed ByCSCD
CSCD IDCSCD:5129071
Contribution Rank1
Funding Organization机器人学国家重点实验室开放基金(No.RLO201005);辽宁省博士启动基金(No.20101125-YOL6170601)。
Keyword红外图像分割 模糊c均值算法 聚类中心 轮廓指标
Abstract分类数和初始聚类中心的选取对红外图像的分割结果有较大的影响。传统的模糊C均值算法的分类数和聚类中心往往设定为经验值。为获得最佳的分类数,提出采用轮廓指标确定出较理想的分类数。针对传统的模糊C均值聚类算法对初始聚类中心比较敏感的问题,提出了基于直方图灰度值的最小最大距离法来确定初始聚类中心。实验结果表明该方法有效可行。
Other AbstractThe numbers of clusters and clustering center have a great effect on the selection of image segmentation results. The traditional Fuzzy C-Means algorithm always adopts empirical values as the number of clusters and clustering center. In order to determine the optimal number of clusters, the validity measure variable of Global Silhouette Index is employed. The most important disadvantage of Fuzzy C-Means clustering is that it generally does not give proper clustering center in the first run. A method which is the minimum-maximum distance based on the gray value of histogram to compute the original clustering center is put forward. Experimental results show that the method is effective and efficient.
Language中文
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/15108
Collection机器人学研究室
Affiliation1.沈阳理工大学信息科学与工程学院
2.中国科学院沈阳自动化研究所机器人学国家重点实验室
Recommended Citation
GB/T 7714
魏英姿,李静静. 红外图像分割的改进模糊C均值聚类方法[J]. 计算机工程与应用,2014,50(9):163-166.
APA 魏英姿,&李静静.(2014).红外图像分割的改进模糊C均值聚类方法.计算机工程与应用,50(9),163-166.
MLA 魏英姿,et al."红外图像分割的改进模糊C均值聚类方法".计算机工程与应用 50.9(2014):163-166.
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