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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
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
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Appears in Collections:Computer Science publications

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