Realistic action recognition via sparsely-constructed Gaussian processes
文献类型：期刊论文
| 作者 | Liu, Li1,2; Shao, Ling1,2; Zheng, Feng2; Li, Xuelong3
|
| 刊名 | pattern recognition
 |
| 出版日期 | 2014-12-01 |
| 卷号 | 47期号:12页码:3819-3827 |
| 关键词 | Action recognition Gaussian processes l(1) construction Local approximation |
| ISSN号 | 0031-3203 |
| 产权排序 | 3 |
| 合作状况 | 国际 |
| 英文摘要 | realistic action recognition has been one of the most challenging research topics in computer vision. the existing methods are commonly based on non-probabilistic classification, predicting category labels but not providing an estimation of uncertainty. in this paper, we propose a probabilistic framework using gaussian processes (gps), which can tackle regression problems with explicit uncertain models, for action recognition. a major challenge for gps when applied to large-scale realistic data is that a large covariance matrix needs to be inverted during inference. additionally, from the manifold perspective, the intrinsic structure of the data space is only constrained by a local neighborhood and data relationships with far-distance usually can be ignored. thus, we design our gps covariance matrix via the proposed l(1) construction and a local approximation (la) covariance weight updating method, which are demonstrated to be robust to data noise, automatically sparse and adaptive to the neighborhood. extensive experiments on four realistic datasets, i.e., ucf youtube, ucf sports, hollywood2 and hmdb51, show the competitive results of l(1)-gps compared with state-of-the-art methods on action recognition tasks. (c) 2014 elsevier ltd. all rights reserved. |
| WOS标题词 | science & technology ; technology |
| 类目[WOS] | computer science, artificial intelligence ; engineering, electrical & electronic |
| 研究领域[WOS] | computer science ; engineering |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000342870900007 |
| 公开日期 | 2015-03-19 |
| 源URL | [http://ir.opt.ac.cn/handle/181661/22419]  |
| 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.Nanjing Univ Informat Sci & Technol, Coll Elect & Informat Engn, Nanjing 210044, Jiangsu, Peoples R China 2.Univ Sheffield, Dept Elect & Elect Engn, Sheffield S1 3JD, S Yorkshire, England 3.Chinese Acad Sci, XIOPM, State Key Lab Transient Opt & Photon, Xian 710119, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Li,Shao, Ling,Zheng, Feng,et al. Realistic action recognition via sparsely-constructed Gaussian processes[J]. pattern recognition,2014,47(12):3819-3827. |
| APA | Liu, Li,Shao, Ling,Zheng, Feng,&Li, Xuelong.(2014).Realistic action recognition via sparsely-constructed Gaussian processes.pattern recognition,47(12),3819-3827. |
| MLA | Liu, Li,et al."Realistic action recognition via sparsely-constructed Gaussian processes".pattern recognition 47.12(2014):3819-3827. |
入库方式： OAI收割
来源：西安光学精密机械研究所
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