Learning semantic context feature-tree for action recognition via nearest neighbor fusion-Reference-Cited by-同舟云学术

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Learning semantic context feature-tree for action recognition via nearest neighbor fusion

Published:2016-08 Issue: Volume:201 Page:1-11
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Zhou Tongchi,Li Nijun,Cheng Xu,Xu Qinjun,Zhou Lin,Wu Zhenyang

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference41 articles.

1. A survey of video datasets for human action and activity recognition;Chaquet;Comput. Vis. Image Underst.,2013

2. Mining spatiotemporal video patterns towards robust action retrieval;Cao;Neurocomputing,2013

3. H. Wang, M. M. Ullah, A. Kläser, et al., Evaluation of local spatio-temporal features for action recognition, in: Proceedings of the British Machine Vision Conference, 2009, pp. 1–11.

4. I. Laptev, M. Marszałek, C. Schmid, et al., Learning realistic human actions from movies, in: Proceedings of the Conference on Computer Vision and Pattern Recognition, 2008, pp. 1–8.

5. P. Dollár, V. Rabaud, G. Cottrell, et al., Behavior recognition via sparse spatio-temporal features, in: Proceedings of the Performance Evaluation of Tracking and Surveillance Workshop, 2005, 65–72.

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