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Rank Preserving Sparse Learning for Kinect Based Scene Classification

文献类型：期刊论文

作者Tao, Dapeng1; Jin, Lianwen1; Yang, Zhao1; Li, Xuelong2
刊名ieee transactions on cybernetics
出版日期2013-10-01
卷号43期号:5页码:1406-1417
关键词Dimension reduction Kinect sensor rank preserving and sparse learning RGB-D sensor scene classification
英文摘要with the rapid development of the rgb-d sensors and the promptly growing population of the low-cost microsoft kinect sensor, scene classification, which is a hard, yet important, problem in computer vision, has gained a resurgence of interest recently. that is because the depth of information provided by the kinect sensor opens an effective and innovative way for scene classification. in this paper, we propose a new scheme for scene classification, which applies locality-constrained linear coding (llc) to local sift features for representing the rgb-d samples and classifies scenes through the cooperation between a new rank preserving sparse learning (rpsl) based dimension reduction and a simple classification method. rpsl considers four aspects: 1) it preserves the rank order information of the within-class samples in a local patch; 2) it maximizes the margin between the between-class samples on the local patch; 3) the l1-norm penalty is introduced to obtain the parsimony property; and 4) it models the classification error minimization by utilizing the least-squares error minimization. experiments are conducted on the nyu depth v1 dataset and demonstrate the robustness and effectiveness of rpsl for scene classification.
WOS标题词science & technology ; technology
类目[WOS]computer science, artificial intelligence ; computer science, cybernetics
研究领域[WOS]computer science
关键词[WOS]nonlinear dimensionality reduction ; texture classification ; face recognition ; features ; representation ; segmentation ; selection ; manifold ; scale ; shape
收录类别SCI ; EI
语种英语
WOS记录号WOS:000324586700009
公开日期2015-06-30
源URL[http://ir.opt.ac.cn/handle/181661/24018]  
专题西安光学精密机械研究所_光学影像学习与分析中心
作者单位1.S China Univ Technol, Sch Elect & Informat Engn, Guangzhou 510640, Peoples R China
2.Chinese Acad Sci, Ctr OPT IMagery Anal & Learning OPTIMAL, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Xian 710119, Peoples R China
推荐引用方式
GB/T 7714		 Tao, Dapeng,Jin, Lianwen,Yang, Zhao,et al. Rank Preserving Sparse Learning for Kinect Based Scene Classification[J]. ieee transactions on cybernetics,2013,43(5):1406-1417.
APA Tao, Dapeng,Jin, Lianwen,Yang, Zhao,&Li, Xuelong.(2013).Rank Preserving Sparse Learning for Kinect Based Scene Classification.ieee transactions on cybernetics,43(5),1406-1417.
MLA Tao, Dapeng,et al."Rank Preserving Sparse Learning for Kinect Based Scene Classification".ieee transactions on cybernetics 43.5(2013):1406-1417.

入库方式： OAI收割

来源：西安光学精密机械研究所

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