计算机工程与应用2017，Vol.53Issue(13)：16-20,5.DOI:10.3778/j.issn.1002-8331.1703-0011
基于改进的相似度度量的谱聚类图像分割方法
Image segmentation method based on improved similarity measure of spectral clustering
摘要
Abstract
Considering the low accuracy of image segmentation method of traditional spectral clustering, an improved similarity measure of spectral clustering is proposed. Firstly, an image is made up of some superpixels by the pre-process of superpixels segmentation algorithm, and a graph based on superpixels is constructed. Secondly, similarity matrix is obtained by the similarity calculation of superpixels, which fully considers the features of superpixels including covari-ance descriptor, color information, texture information and edge information. Finally, NJW algorithm is used to segment the graph based of superpixels. Compared with current unsupervised segmentation algorithm, a lot of experiment results show that the proposed approach has higher segmentation accuracy. Besides, the object marked by user can be segmented precisely using proposed approach.关键词
谱聚类/图像分割/相似度度量/超像素/协方差/NJW算法Key words
spectral clustering/image segmentation/similarity measure/superpixels/covariance/NJW algorithm分类
信息技术与安全科学引用本文复制引用
邹旭华,叶晓东,谭治英,陆凯..基于改进的相似度度量的谱聚类图像分割方法[J].计算机工程与应用,2017,53(13):16-20,5.基金项目
国家自然科学基金(No.61401437). （No.61401437）
