| Rank Preserving Sparse Learning for Kinect Based Scene Classification | |
Tao, Dapeng1; Jin, Lianwen1; Yang, Zhao1; Li, Xuelong2
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| 2013-10-01 | |
| 发表期刊 | IEEE TRANSACTIONS ON CYBERNETICS
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| 卷号 | 43期号:5页码:1406-1417 |
| 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. |
| 文章类型 | Article |
| 关键词 | Dimension Reduction Kinect Sensor Rank Preserving And Sparse Learning Rgb-d Sensor Scene Classification |
| WOS标题词 | Science & Technology ; Technology |
| DOI | 10.1109/TCYB.2013.2264285 |
| 收录类别 | SCI ; EI |
| 关键词[WOS] | NONLINEAR DIMENSIONALITY REDUCTION ; TEXTURE CLASSIFICATION ; FACE RECOGNITION ; FEATURES ; REPRESENTATION ; SEGMENTATION ; SELECTION ; MANIFOLD ; SCALE ; SHAPE |
| 语种 | 英语 |
| WOS研究方向 | Computer Science |
| WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Cybernetics |
| WOS记录号 | WOS:000324586700009 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.opt.ac.cn/handle/181661/24018 |
| 专题 | 光谱成像技术研究室 |
| 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. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| Rank Preserving Spar（11407KB） | 期刊论文 | 出版稿 | 限制开放 | CC BY | 请求全文 | |
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