计算机工程与应用2016，Vol.52Issue(19)：171-178,8.DOI:10.3778/j.issn.1002-8331.1602-0205
核空间直觉模糊局部C-均值聚类分割算法研究
Kernel space intuitionistic fuzzy local C-means clustering segmentation algorithm research
摘要
Abstract
In view of the shortcomings of the existing intuitionistic fuzzy C-means clustering, nonlinear function is adopted to map data samples from Euclidean space to high dimensional feature space of Hilbert, and kernel space intuitionistic fuzzy clustering algorithm is gotten. At the same time, taking account the interaction of neighboring pixels, the neighbor-hood pixels are integrated into objective function optimization of kernel space intuitionistic fuzzy clustering algorithm, and kernel space intuitionistic fuzzy clustering segmentation with pixels local information is obtained by mathematical deduction. The test results of graph segmentation show that kernel space intuitionistic fuzzy C-means clustering algorithm is more satisfactory in segmentation results compared with the existing intuitionistic fuzzy C-means clustering segmenta-tion method, and the kernel space intuitionistic fuzzy C-means segmentation method with local information is shown to be more robust.关键词
直觉模糊聚类/核空间/局部信息Key words
intuitionistic fuzzy clustering/kernel space/local information分类
信息技术与安全科学引用本文复制引用
杜朵朵,吴成茂..核空间直觉模糊局部C-均值聚类分割算法研究[J].计算机工程与应用,2016,52(19):171-178,8.基金项目
国家自然科学基金重点项目（No.61136002）；陕西省教育厅科学研究计划资助项目（No.2015JK1654）；陕西省自然科学基金（No.2014JM8331，No.2014JQ5138，No.2014JM8307）。 （）
