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Abstract
Human action recognition is an important problem in computer vision, which has been applied to many applications. However, how to learn an accurate and discriminative representation of videos based on the features extracted from videos still remains to be a challenging problem. In this paper, we propose a novel method named low-rank representation based action recognition to recognize human actions. Given a dictionary, low-rank representation aims at finding the lowestrank representation of all data, which can capture the global data structures. According to its characteristics, low-rank representation is robust against noises. Experimental results demonstrate the effectiveness of the proposed approach on several publicly available datasets.
Original language | English |
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Number of pages | 7 |
Publication status | Published - 2014 |
Event | 2014 International Joint Conference on Neural Networks - Beijing International Convention Center, 8 Beichen East Road, Chaoyang District, Beijing, China, Beijing, China Duration: 06 Jul 2014 → 11 Jul 2014 |
Conference
Conference | 2014 International Joint Conference on Neural Networks |
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Abbreviated title | IJCNN 2014 |
Country/Territory | China |
City | Beijing |
Period | 06/07/2014 → 11/07/2014 |
Other | International Joint Conference on Neural Networks is the largest technical event in the field of neural networks, jointly organized by IEEE Computational Intelligence Society and Intenational Neural Network Society. In 2014, International Joint Conference on Neural Networks will be part of the 2104 IEEE World Congress on Computational Intelligence. |
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R1118ECI: Centre for Secure Information Technologies (CSIT)
McCanny, J. V., Cowan, C., Crookes, D., Fusco, V., Linton, D., Liu, W., Miller, P., O'Neill, M., Scanlon, W. & Sezer, S.
01/08/2009 → 30/06/2014
Project: Research