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https://hdl.handle.net/2440/82695
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Type: | Conference paper |
Title: | 3D R transform on spatio-temporal interest points for action recognition |
Author: | Yuan, Chunfeng Li, Xi Hu, Weiming Ling, Haibin Maybank, Steve |
Citation: | Proceedings, 2013 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013, 23-28 June 2013, Portland, Oregon, USA: pp. 724-730 |
Publisher: | IEEE |
Issue Date: | 2013 |
ISBN: | 9780769549897 |
ISSN: | 1063-6919 |
Conference Name: | IEEE Conference on Computer Vision and Pattern Recognition (26th : 2013 : Portland, Oregon) CVPR 2013 |
School/Discipline: | School of Computer Science |
Statement of Responsibility: | Chunfeng Yuan, Xi Li, Weiming Hu, Haibin Ling, and Stephen Maybank |
Abstract: | Spatio-temporal interest points serve as an elementary building block in many modern action recognition algorithms, and most of them exploit the local spatio-temporal volume features using a Bag of Visual Words (BOVW) representation. Such representation, however, ignores potentially valuable information about the global spatio-temporal distribution of interest points. In this paper, we propose a new global feature to capture the detailed geometrical distribution of interest points. It is calculated by using the R transform which is defined as an extended 3D discrete Radon transform, followed by applying a two-directional two-dimensional principal component analysis. Such R feature captures the geometrical information of the interest points and keeps invariant to geometry transformation and robust to noise. In addition, we propose a new fusion strategy to combine the R feature with the BOVW representation for further improving recognition accuracy. We utilize a context-aware fusion method to capture both the pairwise similarities and higher-order contextual interactions of the videos. Experimental results on several publicly available datasets demonstrate the effectiveness of the proposed approach for action recognition. |
Rights: | © 2013 IEEE |
DOI: | 10.1109/CVPR.2013.99 |
Description (link): | http://www.pamitc.org/cvpr13/ |
Appears in Collections: | Computer Science publications |
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