NDLI: Cross-View Action Recognition over Heterogeneous Feature Spaces

Content Provider	IEEE Xplore Digital Library
Author	Xinxiao Wu Han Wang Cuiwei Liu Yunde Jia
Copyright Year	2013
Description	Author affiliation: Beijing Lab. of Intell. Inf. Technol., Beijing Inst. of Technol., Beijing, China (Xinxiao Wu; Han Wang; Cuiwei Liu; Yunde Jia)
Abstract	In cross-view action recognition, "what you saw" in one view is different from "what you recognize" in another view. The data distribution even the feature space can change from one view to another due to the appearance and motion of actions drastically vary across different views. In this paper, we address the problem of transferring action models learned in one view (source view) to another different view (target view), where action instances from these two views are represented by heterogeneous features. A novel learning method, called Heterogeneous Transfer Discriminantanalysis of Canonical Correlations (HTDCC), is proposed to learn a discriminative common feature space for linking source and target views to transfer knowledge between them. Two projection matrices that respectively map data from source and target views into the common space are optimized via simultaneously minimizing the canonical correlations of inter-class samples and maximizing the intraclass canonical correlations. Our model is neither restricted to corresponding action instances in the two views nor restricted to the same type of feature, and can handle only a few or even no labeled samples available in the target view. To reduce the data distribution mismatch between the source and target views in the common feature space, a nonparametric criterion is included in the objective function. We additionally propose a joint weight learning method to fuse multiple source-view action classifiers for recognition in the target view. Different combination weights are assigned to different source views, with each weight presenting how contributive the corresponding source view is to the target view. The proposed method is evaluated on the IXMAS multi-view dataset and achieves promising results.
Sponsorship	IEEE Comput. Soc.
Starting Page	609
Ending Page	616
File Size	340041
Page Count	8
File Format	PDF
ISBN	9781479928408
ISSN	15505499
DOI	10.1109/ICCV.2013.81
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-01
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Correlation Training Training data Target recognition Optical imaging Joints Learning systems
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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