KSII Transactions on Internet and Information Systems (TIIS)

  • 제11권3호
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  • Pages.1614-1632
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  • 2017
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  • 1976-7277(pISSN)
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  • 1976-7277(eISSN)
한국인터넷정보학회 (Korean Society for Internet Information)
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A Tree Regularized Classifier-Exploiting Hierarchical Structure Information in Feature Vector for Human Action Recognition

  • Luo, Huiwu (National Key Laboratory of Automatic Target Recognition (ATR), School of Electronic Science and Engineering, National University of Defense Technology) ;
  • Zhao, Fei (National Key Laboratory of Automatic Target Recognition (ATR), School of Electronic Science and Engineering, National University of Defense Technology) ;
  • Chen, Shangfeng (National Key Laboratory of Automatic Target Recognition (ATR), School of Electronic Science and Engineering, National University of Defense Technology) ;
  • Lu, Huanzhang (National Key Laboratory of Automatic Target Recognition (ATR), School of Electronic Science and Engineering, National University of Defense Technology)
  • 투고 : 2016.10.03
  • 심사 : 2016.12.27
  • 발행 : 2017.03.31
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초록

Bag of visual words is a popular model in human action recognition, but usually suffers from loss of spatial and temporal configuration information of local features, and large quantization error in its feature coding procedure. In this paper, to overcome the two deficiencies, we combine sparse coding with spatio-temporal pyramid for human action recognition, and regard this method as the baseline. More importantly, which is also the focus of this paper, we find that there is a hierarchical structure in feature vector constructed by the baseline method. To exploit the hierarchical structure information for better recognition accuracy, we propose a tree regularized classifier to convey the hierarchical structure information. The main contributions of this paper can be summarized as: first, we introduce a tree regularized classifier to encode the hierarchical structure information in feature vector for human action recognition. Second, we present an optimization algorithm to learn the parameters of the proposed classifier. Third, the performance of the proposed classifier is evaluated on YouTube, Hollywood2, and UCF50 datasets, the experimental results show that the proposed tree regularized classifier obtains better performance than SVM and other popular classifiers, and achieves promising results on the three datasets.

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