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|Title:||3D R transform on spatio-temporal interest points for action recognition|
|Citation:||Proceedings, 2013 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013, 23-28 June 2013, Portland, Oregon, USA: pp. 724-730|
|Conference Name:||IEEE Conference on Computer Vision and Pattern Recognition (26th : 2013 : Portland, Oregon)|
|School/Discipline:||School of Computer Science|
|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|
|Appears in Collections:||Computer Science publications|
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