期刊
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
卷 47, 期 4, 页码 617-627出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMC.2016.2617465
关键词
Computer interfaces; parameter estimation; pattern recognition
资金
- National Natural Science Foundation of China [61572486, 61671480, 6140021567, 6140051238, 61301242]
- Yunnan Natural Science Funds [2016FB105]
- Guangdong Natural Science Funds [2014A030310252, 2015A030313744]
- Shenzhen Technology Project [JCYJ20140901003939001, JSGG20160331185256983, JSGG20140703092631382, JCYJ20140417113430736, JCYJ20140901003939013]
- Opening Project of State Key Laboratory of Digital Publishing Technology
- Program for Excellent Young Talents of Yunnan University
- Special Program of Guangdong Frontier and Key Technological Innovation [2016B010108010]
- Key Laboratory of Human-Machine Intelligence-Synergy Systems, Chinese Academy of Sciences [2014DP173025]
- Fundamental Research Funds for the Central Universities China University of Petroleum (East China) [14CX02203A]
The ever-growing popularity of Kinect and inertial sensors has prompted intensive research efforts on human action recognition. Since human actions were extracted from Kinect and inertial sensors, they can be characterized by multiple feature representations. By encoding the multiview features into a unified space, it could be optimal for human action recognition. In this paper, we propose a new unsupervised feature fusion method termed multiview Cauchy estimator feature embedding (MCEFE) for human action recognition. By minimizing empirical risk, MCEFE integrates the encoded complementary information in multiple views to find the unified data representation and the projection matrices. To enhance robustness to outliers, the Cauchy estimator is imposed on the reconstruction error. Furthermore, ensemble manifold regularization is enforced on the projection matrices to encode the correlations between different views and avoid overfitting. Experiments are conducted on the new Chinese Academy of Sciences-Yunnan University-multimodal human action database to demonstrate the effectiveness and robustness of MCEFE for human action recognition.
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