caa a22 4500 605446717 CHVBK 20210128100128.0 cr unu---uuuuu 210128e20150101xx s 000 0 eng 10.1007/s11042-014-2010-6 doi (NATIONALLICENCE)springer-10.1007/s11042-014-2010-6 Max-margin adaptive model for complex video pattern recognition [Elektronische Daten] [Litao Yu, Jie Shao, Xin-Shun Xu, Heng Shen] Patternrecognitionmodels are usually used in a variety of applications ranging from video concept annotation to event detection. In this paper we propose a new framework called the max-margin adaptive (MMA) model for complex video pattern recognition, which can utilize a large number of unlabeled videos to assist the model training. The MMA model considers the data distribution consistence between labeled training videos and unlabeled auxiliary ones from the statistical perspective by learning an optimal mapping function which also broadens the margin between positive labeled videos and negative labeled videos to improve the robustness of the model. The experiments are conducted on two public datasets including CCV for video object/event detection and HMDB for action recognition. Our results demonstrate that the proposed MMA model is very effective on complex video pattern recognition tasks, and outperforms the state-of-the-art algorithms. Springer Science+Business Media New York, 2014 Video pattern recognition nationallicence Max-margin adaptive model nationallicence Event detection nationallicence Yu Litao School of Information Technology and Electrical Engineering, The University of Queensland, 4072, Brisbane, QLD, Australia aut Shao Jie Department of Computer Science, National University of Singapore, 117417, Singapore, Singapore aut Xu Xin-Shun School of Computer Science and Technology, Shandong University, 250101, Jinan, Shandong, People's Republic of China aut Shen Heng School of Information Technology and Electrical Engineering, The University of Queensland, 4072, Brisbane, QLD, Australia aut Multimedia Tools and Applications Springer US; http://www.springer-ny.com 74/2(2015-01-01), 505-521 1380-7501 74:2<505 2015 74 11042 https://doi.org/10.1007/s11042-014-2010-6 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s11042-014-2010-6 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Yu Litao School of Information Technology and Electrical Engineering, The University of Queensland, 4072, Brisbane, QLD, Australia aut NATIONALLICENCE 700 1- Shao Jie Department of Computer Science, National University of Singapore, 117417, Singapore, Singapore aut NATIONALLICENCE 700 1- Xu Xin-Shun School of Computer Science and Technology, Shandong University, 250101, Jinan, Shandong, People's Republic of China aut NATIONALLICENCE 700 1- Shen Heng School of Information Technology and Electrical Engineering, The University of Queensland, 4072, Brisbane, QLD, Australia aut NATIONALLICENCE 773 0- Multimedia Tools and Applications Springer US; http://www.springer-ny.com 74/2(2015-01-01), 505-521 1380-7501 74:2<505 2015 74 11042