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Visual Clustering based on Kernel Sparse Representation on Grassmann Manifolds
Liu TC(刘天赐); Shi ZL(史泽林); Liu YP(刘云鹏)
Department光电信息技术研究室
Conference Name7th IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017
Conference DateJuly 31 - August 4, 2017
Conference PlaceHawaii, USA
Author of SourceIEEE Robotics and Automation Society
Source Publication2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017
PublisherIEEE
Publication PlaceNew York
2017
Pages920-925
Indexed ByEI ; CPCI(ISTP)
EI Accession number20183905873557
WOS IDWOS:000447628700167
Contribution Rank1
ISBN978-1-5386-0489-2
KeywordVisual Clustering Grassmann Manifold Sparse Representation Kernel Method
Abstract

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. In this paper, we propose a novel algorithm termed as kernel sparse subspace clustering on the Grassmann manifold (GKSSC) which embeds the Grassmann manifold into a Reproducing Kernel Hilbert Space (RKHS) 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, experimental results of clustering accuracy on the prevalent public dataset outperform state-of-the-art algorithms by more than 90 percent and the robustness of our algorithm is demonstrated as well.

Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/21302
Collection光电信息技术研究室
Corresponding AuthorLiu TC(刘天赐)
Affiliation1.Key Laboratory of Opto-Electronic Information Processing, CAS, Shenyang 110016
2.The Key Lab of Image Understanding and Computer Vision, Liaoning Province, Shenyang 110016
3.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016
4.University of Chinese Academy of Sciences, Beijing 100049
Recommended Citation
GB/T 7714
Liu TC,Shi ZL,Liu YP. Visual Clustering based on Kernel Sparse Representation on Grassmann Manifolds[C]//IEEE Robotics and Automation Society. New York:IEEE,2017:920-925.
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