Boosted Exemplar Learning for Action Recognition and Annotation
文献类型：期刊论文
| 作者 | Zhang, Tianzhu1,2 ; Liu, Jing1,2; Liu, Si1,2; Xu, Changsheng1,2; Lu, Hanqing1,2
|
| 刊名 | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
 |
| 出版日期 | 2011-07-01 |
| 卷号 | 21期号:7页码:853-866 |
| 关键词 | Action annotation action recognition AdaBoost mi-SVM multiple instance learning (MIL) |
| 英文摘要 | Human action recognition and annotation is an active research topic in computer vision. How to model various actions, varying with time resolution, visual appearance, and others, is a challenging task. In this paper, we propose a boosted exemplar learning (BEL) approach to model various actions in a weakly supervised manner, i.e., only action bag-level labels are provided but action instance level ones are not. The proposed BEL method can be summarized as three steps. First, for each action category, amount of class-specific candidate exemplars are learned through an optimization formulation considering their discrimination and co-occurrence. Second, each action bag is described as a set of similarities between its instances and candidate exemplars. Instead of simply using a heuristic distance measure, the similarities are decided by the exemplar-based classifiers through the multiple instance learning, in which a positive (or negative) video or image set is deemed as a positive (or negative) action bag and those frames similar to the given exemplar in Euclidean Space as action instances. Third, we formulate the selection of the most discriminative exemplars into a boosted feature selection framework and simultaneously obtain an action bag-based detector. Experimental results on two publicly available datasets: the KTH dataset and Weizmann dataset, demonstrate the validity and effectiveness of the proposed approach for action recognition. We also apply BEL to learn representations of actions by using images collected from the Web and use this knowledge to automatically annotate action in YouTube videos. Results are very impressive, which proves that the proposed algorithm is also practical in unconstraint environments. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Engineering, Electrical & Electronic |
| 研究领域[WOS] | Engineering |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000293684300001 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/3324]  |
| 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, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China 2.China Singapore Inst Digital Media, Singapore 119613, Singapore |
| 推荐引用方式 GB/T 7714 | Zhang, Tianzhu,Liu, Jing,Liu, Si,et al. Boosted Exemplar Learning for Action Recognition and Annotation[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2011,21(7):853-866. |
| APA | Zhang, Tianzhu,Liu, Jing,Liu, Si,Xu, Changsheng,&Lu, Hanqing.(2011).Boosted Exemplar Learning for Action Recognition and Annotation.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,21(7),853-866. |
| MLA | Zhang, Tianzhu,et al."Boosted Exemplar Learning for Action Recognition and Annotation".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 21.7(2011):853-866. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。