[1]许 芹,唐敦兵,蔡祺祥.改进的快速模糊C均值聚类图像分割算法[J].南京理工大学学报(自然科学版),2016,40(03):309.[doi:10.14177/j.cnki.32-1397n.2016.40.03.010]
 Xu Qin,Tang Dunbing,Cai Qixiang.Improved fast fuzzy C-means clustering algorithm for image segmentation[J].Journal of Nanjing University of Science and Technology,2016,40(03):309.[doi:10.14177/j.cnki.32-1397n.2016.40.03.010]
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改进的快速模糊C均值聚类图像分割算法
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
40卷
期数:
2016年03期
页码:
309
栏目:
出版日期:
2016-06-30

文章信息/Info

Title:
Improved fast fuzzy C-means clustering algorithm for image segmentation
文章编号:
1005-9830(2016)03-0309-06
作者:
许 芹1唐敦兵2蔡祺祥2
1.安徽科技学院 电气与电子工程学院,安徽 滁州 233100; 2.南京航空航天大学 机电学院,江苏 南京 210016
Author(s):
Xu Qin1Tang Dunbing2Cai Qixiang2
1.College of Electrical and Electronic Engineering,Anhui Science and Technology University, Chuzhou 233100,China; 2.College of Mechanical and Electrical Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China
关键词:
模糊聚类 C均值聚类 图像分割 像素空间 灰度直方图 特征空间 曲线拟合方法 聚类数 初始聚类中心
Keywords:
fuzzy clustering C-means clustering image segmentation pixel space gray histogram feature space curve fitting method clustering number initial clustering center
分类号:
TP391.4
DOI:
10.14177/j.cnki.32-1397n.2016.40.03.010
摘要:
为了提高图像分割的运算速度,该文在将传统模糊C均值(FCM)聚类算法应用于图像自动分割的基础上,提出一种改进的快速图像分割算法。将图像从像素空间映射至其对应的灰度直方图特征空间,实现在特征空间进行数据聚类分析以减少聚类样本数量。依据灰度直方图特性,通过曲线拟合方法获得图像的聚类数及初始聚类中心。实验结果表明,在有效分割图像的基础上,该算法的运算迭代次数减少了约10%,运行时间减小了约6%。
Abstract:
In order to improve the speed of image segmentation,an improved fast image segmentation algorithm is proposed based on the application of conventional fuzzy C-means(FCM)clustering algorithm for image automatic segmentation.An image is mapped to the corresponding gray histogram feature space from the pixel space.Data clustering analysis is realized in the feature space and the amount of the clustering sample is reduced.According to the characteristics of the gray histogram,the clustering number and the initial clustering center are obtained by curve fitting method.The experimental results show that the iterative number is reduced by 10% and the run time is reduced by 6% and effective image segmentation is realized.

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相似文献/References:

[1]胡钟山,丁震,杨静宇,等.一种改进的 Fuzzy c-means 聚类算法[J].南京理工大学学报(自然科学版),1997,(04):53.
 Hu ZhongshanDing ZhenYang JingyuTang ZhenmingWu Yongge.A Modified Fuzzy c means Clustering Algorithm[J].Journal of Nanjing University of Science and Technology,1997,(03):53.

备注/Memo

备注/Memo:
收稿日期:2016-01-28 修回日期:2016-03-21
基金项目:国家自然科学基金(51175262); 安徽省高校自然科学研究项目(KJ2013B075); 安徽科技学院青年科学基金(ZRC2013338); 安徽科技学院重点建设学科(AKZDXK2015C02)
作者简介:许芹(1980-),女,硕士,主要研究方向:信息与信号处理,E-mail:toby_sh@163.com。
引文格式:许芹,唐敦兵,蔡祺祥.改进的快速模糊C均值聚类图像分割算法[J].南京理工大学学报,2016,40(3):309-314.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2016-06-30