| Realistic action recognition via sparsely-constructed Gaussian processes | |
Liu, Li1,2; Shao, Ling1,2; Zheng, Feng2; Li, Xuelong3
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| 作者部门 | 光学影像学习与分析中心 |
| 2014-12-01 | |
| 发表期刊 | PATTERN RECOGNITION
 |
| ISSN | 0031-3203 |
| 卷号 | 47期号:12页码:3819-3827 |
| 产权排序 | 3 |
| 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. |
| 文章类型 | Article |
| 关键词 | Action Recognition Gaussian Processes l(1) Construction Local Approximation |
| WOS标题词 | Science & Technology ; Technology |
| DOI | 10.1016/j.patcog.2014.07.006 |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS研究方向 | Computer Science ; Engineering |
| WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
| WOS记录号 | WOS:000342870900007 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.opt.ac.cn/handle/181661/22419 |
| 专题 | 光谱成像技术研究室 |
| 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. |
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| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| Realistic action rec（1829KB） | 期刊论文 | 出版稿 | 限制开放 | CC BY | 请求全文 | |
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