Abstract
| Original language | English |
|---|---|
| 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 |
|---|---|
| Abbreviated title | IJCNN 2014 |
| Country | 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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Low-rank representation based action recognition. / Zhang, Xiangrong; Yang, Yang; Jia, Hanghua; Zhou, Huiyu; Jiao, Licheng.
2014. Paper presented at 2014 International Joint Conference on Neural Networks, Beijing, China.Research output: Contribution to conference › Paper
TY - CONF
T1 - Low-rank representation based action recognition
AU - Zhang, Xiangrong
AU - Yang, Yang
AU - Jia, Hanghua
AU - Zhou, Huiyu
AU - Jiao, Licheng
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
M3 - Paper
ER -