同济大学学报（自然科学版）2013，Vol.41Issue(2)：271-276,6.DOI:10.3969/j.issn.0253-374x.2013.02.020
一种基于低秩描述的图像集分类方法
Image Set Classification Based on Low-rank Representation
摘要
Abstract
Graph embedding discriminant analysis on manifold approach represents each image set as a subspace on manifold. It maps the manifold to a more discriminative one with geometrical structure and local information preserved. However, its accuracy critically depends on the number of local neighbours when constructing similarity graph. This paper presents a novel approach with fixed neighbour numbers to implement graph embedding Grassmannian discriminant analysis based on low-rank representation (LRR) for each image set. After the low-rank components of each set being recovered, to preserve the nearest neighbour structure of nodes with the same label and all the different label information during the manifold mapping can always achieve the best performance. Experiments on two image datasets (15-scenes categories and CaltechlOl) show that the proposed method greatly improves the classification accuracy of image sets.关键词
流形鉴别分析/低秩分解/图像集/局部图嵌入Key words
manifold discriminant analysis/ low-rank representation/ image set/ graph embedding分类
信息技术与安全科学引用本文复制引用
吕煊,王志成,赵卫东,刘玉淑..一种基于低秩描述的图像集分类方法[J].同济大学学报（自然科学版）,2013,41(2):271-276,6.基金项目
国家自然科学基金(61103070) （61103070）
"十二五"国家科技支撑计划(2012BAF10B12) （2012BAF10B12）
上海市科委项目(12dz1125400) （12dz1125400）
中央高校基本科研业务费专项资金(0800219171) （0800219171）
