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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)
作者部门光电信息技术研究室
会议名称7th IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017
会议日期July 31 - August 4, 2017
会议地点Hawaii, USA
会议主办者IEEE Robotics and Automation Society
会议录名称2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017
出版者IEEE
出版地New York
2017
页码920-925
收录类别EI ; CPCI(ISTP)
EI收录号20183905873557
WOS记录号WOS:000447628700167
产权排序1
ISBN号978-1-5386-0489-2
关键词Visual Clustering Grassmann Manifold Sparse Representation Kernel Method
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. 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.

语种英语
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文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/21302
专题光电信息技术研究室
通讯作者Liu TC(刘天赐)
作者单位1.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
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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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