IMR OpenIR
Kernel sparse representation on Grassmann manifolds for visual clustering
Liu, Tianci1,2,3; Shi, Zelin1,3; Liu, Yunpeng1,3
Corresponding AuthorLiu, Tianci(liutianci@sia.cn)
2018-05-01
Source PublicationOPTICAL ENGINEERING
ISSN0091-3286
Volume57Issue:5Pages:10
AbstractImage 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)
KeywordGrassmann manifold visual clustering sparse representation kernel method
Funding OrganizationNational Natural Science Foundation of China ; Common Technical Project of Equipment Development Department
DOI10.1117/1.OE.57.5.053104
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61540069] ; Common Technical Project of Equipment Development Department[Y6K4250401]
WOS Research AreaOptics
WOS SubjectOptics
WOS IDWOS:000435435300013
PublisherSPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.imr.ac.cn/handle/321006/128650
Collection中国科学院金属研究所
Corresponding AuthorLiu, Tianci
Affiliation1.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
Recommended Citation
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.
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