计算机工程与应用2012，Vol.48Issue(10)：183-186,4.DOI:10.3778/j.issn.1002-8331.2012.10.041
基于快速二维熵的加权模糊C均值聚类图像分割
Fast image segmentation of weighted fuzzy C-means clustering based on 2-D entropy
摘要
Abstract
In this paper, an image segmentation algorithm for combining fast two-dimensional entropy and weighted fuzzy C-means(FCM) clustering is proposed. The centers of the object and the background are obtained by applying fast two-dimensions entropy algorithm. Then, the influence of every sample on the classification is characterized by the gray difference between the sample and its neighborhood samples. At last, the segmentation is obtained by weighted fuzzy C-means clustering algorithm. The new algorithm can solve the question that the traditional FCM clustering algorithm is sensitive to the initial value. Moreover, it can overcome the shortage that the traditional clustering algorithm is equally partition to the sample set. The experimental result shows that the algorithm not only has good convergence, but also can effectively segment the target from its background. The new algorithm has the important practical application value.关键词
模糊C均值聚类/二维熵/图像分割Key words
fuzzy C-means clustering/ two-dimensional entropy/ image segmentation分类
信息技术与安全科学引用本文复制引用
沙秀艳,王贞俭..基于快速二维熵的加权模糊C均值聚类图像分割[J].计算机工程与应用,2012,48(10):183-186,4.基金项目
国家自然科学基金(No.11001117) （No.11001117）
山东省高等学校科技计划项目(No.J10LA09) （No.J10LA09）
鲁东大学校基金(No.L20072703,No.L20082703). （No.L20072703,No.L20082703）
