计算机应用研究Issue(1)：291-295,5.DOI:10.3969/j.issn.1001-3695.2016.01.067
一种基于压缩感知理论的纹理分类方法
Texture classification method based on theory of compressed sensing
摘要
Abstract
According to the theories of sparse representation and compressed sensing,this paper presented a simple,novel approach for texture classification based on bag-of-words model.At the feature extraction stage,it extracted a small set of ran-dom features from local image patches.It embedded the random features into a bag-of-words model to perform texture classifi-cation;thus,carried out learning and classification in a compressed domain,yet by leveraging the sparse nature of texture im-ages,our approach outperformed traditional feature extraction methods which involved careful design and complex steps.It conducted extensive experiments on the CUReT databases.Results show that our approach leads to significant improvements in classification accuracy and instantaneity.关键词
稀疏表示/压缩感知/词袋模型/纹理分类Key words
sparse representation/compressed sensing/bag-of-words model/texture classification分类
信息技术与安全科学引用本文复制引用
吴迪..一种基于压缩感知理论的纹理分类方法[J].计算机应用研究,2016,(1):291-295,5.基金项目
国家科技支撑计划资助项目（1214ZGA008）；国家自然科学基金资助项目（61263031）；湖南省重点学科建设项目（081101）；重庆市教委自然科学基金资助项目（KJ1400628）；湖南工程学院博士科研启动基金资助项目 （）
