计算机工程2011，Vol.37Issue(10)：187-188,191,3.DOI:10.3969/j.issn.1000-3428.2011.10.064
基于最大熵的模糊核聚类图像分割方法
Fuzzy Kernel Clustering Image Segmentation Method Based on Maximum Entropy
摘要
Abstract
The traditional clustering method is prone to fall into local extremum. It is bad to classify when the data is linear inseparable. This paper proposes a fuzzy kernel clustering image segmentation method based on maximum entropy. It applies maximum entropy algorithm to obtain the initial centers and maps the sample from the input space to the feature space by introducing Mercer kernel function into the method. It completes image segmentation in the feature space. Experimental result shows that the method can reduce the iteration time and steady the class result, and effectively segment the target from its background.关键词
模糊核聚类/最大熵/特征空间/图像分割Key words
fuzzy kernel clustering/ maximum entropy/ feature space/ image segmentation分类
信息技术与安全科学引用本文复制引用
沙秀艳,辛杰..基于最大熵的模糊核聚类图像分割方法[J].计算机工程,2011,37(10):187-188,191,3.基金项目
国家自然科学基金资助项目(10626046) （10626046）
中国博士后科学基金资助项目(20070410487) （20070410487）
鲁东大学校基金资助项目(L20072703,L20082703) （L20072703,L20082703）
