Boosted multi-class semi-supervised learning for human action recognition
文献类型：期刊论文
| 作者 | Zhang, Tianzhu1,2 ; Liu, Si1,2; Xu, Changsheng1,2; Lu, Hanqing1,2
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| 刊名 | PATTERN RECOGNITION
 |
| 出版日期 | 2011-10-01 |
| 卷号 | 44期号:10-11页码:2334-2342 |
| 关键词 | Semi-supervised learning Action recognition adaBoost.MH co-EM |
| 英文摘要 | Human action recognition is a challenging task due to significant intra-class variations, occlusion, and background clutter. Most of the existing work use the action models based on statistic learning algorithms for classification. To achieve good performance on recognition, a large amount of the labeled samples are therefore required to train the sophisticated action models. However, collecting labeled samples is labor-intensive. To tackle this problem, we propose a boosted multi-class semi-supervised learning algorithm in which the co-EM algorithm is adopted to leverage the information from unlabeled data. Three key issues are addressed in this paper. Firstly, we formulate the action recognition in a multi-class semi-supervised learning problem to deal with the insufficient labeled data and high computational expense. Secondly, boosted co-EM is employed for the semi-supervised model construction. To overcome the high dimensional feature space, weighted multiple discriminant analysis (WMDA) is used to project the features into low dimensional subspaces in which the Gaussian mixture models (GMM) are trained and boosting scheme is used to integrate the subspace models. Thirdly, we present the upper bound of the training error in multi-class framework, which is able to guide the novel classifier construction. In theory, the proposed solution is proved to minimize this upper error bound. Experimental results have shown good performance on public datasets. Crown Copyright (C) 2010 Published by 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:000292849000012 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/3325]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_图像与视频分析团队 |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.China Singapore Inst Digital Media, Singapore 119615, Singapore |
| 推荐引用方式 GB/T 7714 | Zhang, Tianzhu,Liu, Si,Xu, Changsheng,et al. Boosted multi-class semi-supervised learning for human action recognition[J]. PATTERN RECOGNITION,2011,44(10-11):2334-2342. |
| APA | Zhang, Tianzhu,Liu, Si,Xu, Changsheng,&Lu, Hanqing.(2011).Boosted multi-class semi-supervised learning for human action recognition.PATTERN RECOGNITION,44(10-11),2334-2342. |
| MLA | Zhang, Tianzhu,et al."Boosted multi-class semi-supervised learning for human action recognition".PATTERN RECOGNITION 44.10-11(2011):2334-2342. |
入库方式： OAI收割
来源：自动化研究所
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