Laplacian group sparse modeling of human actions.

Xiangrong Zhang, Hao Yang, L. C. Jiao, Yang Yang, Feng Dong

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


Recently, many local-feature based methods have been proposed for feature learning to obtain a better high-level representation of human behavior. Most of the previous research ignores the structural information existing among local features in the same video sequences, while it is an important clue to distinguish ambiguous actions. To address this issue, we propose a Laplacian group sparse coding for human behavior representation. Unlike traditional methods such as sparse coding, our approach prefers to encode a group of relevant features simultaneously and meanwhile allow as less atoms as possible to participate in the approximation so that video-level sparsity is guaranteed. By incorporating Laplacian regularization the method is capable to ensure the similar approximation of closely related local features and the structural information is successfully preserved. Thus, a compact but discriminative human behavior representation is achieved. Besides, the objective of our model is solved with a closed-form solution, which reduces the computational cost significantly. Promising results on several popular benchmark datasets prove the efficiency and effectiveness of our approach.
Original languageEnglish
Pages (from-to)2689-2701
Number of pages13
JournalPattern Recognition
Issue number8
Early online date20 Feb 2014
Publication statusPublished - 31 Aug 2014


  • action recognition
  • high-level representation
  • laplacian group sparse coding
  • structural information


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