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Supervised Dimensionality Reduction on Grassmannian for Image Set Recognition
Liu TC(刘天赐)1,2,3,4,5; Shi ZL(史泽林)1,2,4,5; Liu YP(刘云鹏)1,2,4,5
Department光电信息技术研究室
Source PublicationNEURAL COMPUTATION
ISSN0899-7667
2019
Volume31Issue:1Pages:156-175
Indexed BySCI ; EI
EI Accession number20190206349844
WOS IDWOS:000454696900005
Contribution Rank1
Funding OrganizationInnovation Fund of the Chinese Academy of Sciences
AbstractModeling videos and image sets by linear subspaces has achieved great success in various visual recognition tasks. However, subspaces constructed from visual data are always notoriously embedded in a high-dimensional ambient space, which limits the applicability of existing techniques. This letter explores the possibility of proposing a geometry-aware framework for constructing lower-dimensional subspaces with maximum discriminative power from high-dimensional subspaces in the supervised scenario. In particular, we make use of Riemannian geometry and optimization techniques on matrix manifolds to learn an orthogonal projection, which shows that the learning process can be formulated as an unconstrained optimization problem on a Grassmann manifold. With this natural geometry, any metric on the Grassmann manifold can theoretically be used in our model. Experimental evaluations on several data sets show that our approach results in significantly higher accuracy than other state-of-the-art algorithms.
Language英语
WOS SubjectComputer Science, Artificial Intelligence ; Neurosciences
WOS KeywordGEOMETRY
WOS Research AreaComputer Science ; Neurosciences & Neurology
Funding ProjectInnovation Fund of the Chinese Academy of Sciences[Y8K4160401] ; Innovation Fund of the Chinese Academy of Sciences[Y8K4160401]
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Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/24043
Collection光电信息技术研究室
Affiliation1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
3.University of Chinese Academy of Sciences, Beijing 100049, China
4.Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China
5.and Key Lab of Image Understanding and Computer Vision, Liaoning Province, Shenyang 110016, China
Recommended Citation
GB/T 7714
Liu TC,Shi ZL,Liu YP. Supervised Dimensionality Reduction on Grassmannian for Image Set Recognition[J]. NEURAL COMPUTATION,2019,31(1):156-175.
APA Liu TC,Shi ZL,&Liu YP.(2019).Supervised Dimensionality Reduction on Grassmannian for Image Set Recognition.NEURAL COMPUTATION,31(1),156-175.
MLA Liu TC,et al."Supervised Dimensionality Reduction on Grassmannian for Image Set Recognition".NEURAL COMPUTATION 31.1(2019):156-175.
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