Knowledge Commons of Institute of Automation,CAS
Boosted Exemplar Learning for Action Recognition and Annotation | |
Zhang, Tianzhu1,2; Liu, Jing1,2; Liu, Si1,2; Xu, Changsheng1,2; Lu, Hanqing1,2 | |
发表期刊 | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY |
2011-07-01 | |
卷号 | 21期号:7页码:853-866 |
文章类型 | Article |
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. |
关键词 | Action Annotation Action Recognition Adaboost Mi-svm Multiple Instance Learning (Mil) |
WOS标题词 | Science & Technology ; Technology |
收录类别 | SCI |
语种 | 英语 |
WOS研究方向 | Engineering |
WOS类目 | Engineering, Electrical & Electronic |
WOS记录号 | WOS:000293684300001 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/3324 |
专题 | 紫东太初大模型研究中心_图像与视频分析 |
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 |
第一作者单位 | 模式识别国家重点实验室 |
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. |
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