Low-rank representation based action recognition

Xiangrong Zhang, Yang Yang, Hanghua Jia, Huiyu Zhou, Licheng Jiao

Research output: Contribution to conferencePaper

4 Citations (Scopus)
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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 languageEnglish
Number of pages7
Publication statusPublished - 2014
Event2014 International Joint Conference on Neural Networks - Beijing International Convention Center, 8 Beichen East Road, Chaoyang District, Beijing, China, Beijing, China
Duration: 06 Jul 201411 Jul 2014

Conference

Conference2014 International Joint Conference on Neural Networks
Abbreviated titleIJCNN 2014
CountryChina
CityBeijing
Period06/07/201411/07/2014
OtherInternational 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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Cite this

Zhang, X., Yang, Y., Jia, H., Zhou, H., & Jiao, L. (2014). Low-rank representation based action recognition. Paper presented at 2014 International Joint Conference on Neural Networks, Beijing, China.
Zhang, Xiangrong ; Yang, Yang ; Jia, Hanghua ; Zhou, Huiyu ; Jiao, Licheng. / Low-rank representation based action recognition. Paper presented at 2014 International Joint Conference on Neural Networks, Beijing, China.7 p.
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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.",
author = "Xiangrong Zhang and Yang Yang and Hanghua Jia and Huiyu Zhou and Licheng Jiao",
year = "2014",
language = "English",
note = "2014 International Joint Conference on Neural Networks, IJCNN 2014 ; Conference date: 06-07-2014 Through 11-07-2014",

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Zhang, X, Yang, Y, Jia, H, Zhou, H & Jiao, L 2014, 'Low-rank representation based action recognition', Paper presented at 2014 International Joint Conference on Neural Networks, Beijing, China, 06/07/2014 - 11/07/2014.

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 conferencePaper

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 -

Zhang X, Yang Y, Jia H, Zhou H, Jiao L. Low-rank representation based action recognition. 2014. Paper presented at 2014 International Joint Conference on Neural Networks, Beijing, China.