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Cross-view Action Recognition via Transductive Transfer Learning
Jie Qin; Zhaoxiang Zhang; Yunhong Wang
2013-09-15
Conference NameInternational Conference on Image Processing
Source PublicationICIP 2013
Conference Date15-18 September 2013
Conference PlaceMelbourne, Australia
AbstractHuman action recognition is a hot topic in computer vision field. Various applicable approaches have been proposed to recognize different types of actions. However, the recognition performance deteriorates rapidly when the viewpoint changes. Traditional approaches aim to address the problem by inductive transfer learning, in which target-view samples are manually labeled. In this paper, we present a novel approach for cross-view action recognition based on transductive transfer learning. We address the problem by transferring instances across views. In our settings, both labels of examples from the target view and the corresponding relation between examples from pairwise views are dispensable. Experimental results on the IXMAS multi-view data set demonstrate the effectiveness of our approach, and are comparable to the state of the art.
KeywordTransductive Svm Action Recognition Transfer Learning
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/13286
Collection类脑智能研究中心
Corresponding AuthorZhaoxiang Zhang
Recommended Citation
GB/T 7714
Jie Qin,Zhaoxiang Zhang,Yunhong Wang. Cross-view Action Recognition via Transductive Transfer Learning[C],2013.
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