南京理工大学学报（自然科学版）2016，Vol.40Issue(3)：309-314,6.DOI:10.14177/j.cnki.32-1397n.2016.40.03.010
改进的快速模糊C均值聚类图像分割算法
Improved fast fuzzy C-means clustering algorithm for image segmentation
摘要
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 .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Key words
fuzzy clustering/C-means clustering/image segmentation/pixel space/gray histogram/feature space/curve fitting method/clustering number/initial clustering center分类
信息技术与安全科学引用本文复制引用
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(3):309-314,6.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（ KJ2013 B075）；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 （）
