Kernel sparse representation on Grassmann manifolds for visual clustering

doi:10.1117/1.OE.57.5.053104

IMR OpenIR

	Kernel sparse representation on Grassmann manifolds for visual clustering
	Liu, Tianci 1,2,3; Shi, Zelin 1,3; Liu, Yunpeng 1,3
通讯作者	Liu, Tianci(liutianci@sia.cn)
	2018-05-01
发表期刊	OPTICAL ENGINEERING
ISSN	0091-3286
卷号	57 期号:5 页码:10
摘要	Image sets and videos can be modeled as subspaces, which are actually points on Grassmann manifolds. Clustering of such visual data lying on Grassmann manifolds is a hard issue based on the fact that the state-of-the-art methods are only applied to vector space instead of non-Euclidean geometry. Although there exist some clustering methods for manifolds, the desirable method for clustering on Grassmann manifolds is lacking. We propose an algorithm termed as kernel sparse subspace clustering on the Grassmann manifold, which embeds the Grassmann manifold into a reproducing kernel Hilbert space by an appropriate Gaussian projection kernel. This kernel is applied to obtain kernel sparse representations of data on Grassmann manifolds utilizing the self-expressive property and exploiting the intrinsic Riemannian geometry within data. Although the Grassmann manifold is compact, the geodesic distances between Grassmann points are well measured by kernel sparse representations based on linear reconstruction. With the kernel sparse representations, clustering results of experiments on three prevalent public datasets outperform a number of existing algorithms and the robustness of our algorithm is demonstrated as well. (C) 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)
关键词	Grassmann manifold visual clustering sparse representation kernel method
资助者	National Natural Science Foundation of China ; Common Technical Project of Equipment Development Department
DOI	10.1117/1.OE.57.5.053104
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61540069] ; Common Technical Project of Equipment Development Department[Y6K4250401]
WOS研究方向	Optics
WOS类目	Optics
WOS记录号	WOS:000435435300013
出版者	SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS
引用统计	被引频次：3[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.imr.ac.cn/handle/321006/128650
专题	中国科学院金属研究所
通讯作者	Liu, Tianci
作者单位	1.Chinese Acad Sci, Shenyang Inst Automat, Shenyang, Liaoning, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.Key Lab Optoelect Informat Proc, Shenyang, Liaoning, Peoples R China
推荐引用方式 GB/T 7714	Liu, Tianci,Shi, Zelin,Liu, Yunpeng. Kernel sparse representation on Grassmann manifolds for visual clustering[J]. OPTICAL ENGINEERING,2018,57(5):10.
APA	Liu, Tianci,Shi, Zelin,&Liu, Yunpeng.(2018).Kernel sparse representation on Grassmann manifolds for visual clustering.OPTICAL ENGINEERING,57(5),10.
MLA	Liu, Tianci,et al."Kernel sparse representation on Grassmann manifolds for visual clustering".OPTICAL ENGINEERING 57.5(2018):10.