• DOI:10.1007/978-3-319-54184-6_1
• Corpus ID: 3972877

Sparse Code Filtering for Action Pattern Mining

@inproceedings{Wang2016SparseCF,
  title={Sparse Code Filtering for Action Pattern Mining},
  author={Wei Wang and Yan Yan and Liqiang Nie and Luming Zhang and Stefan Winkler and N. Sebe},
  booktitle={ACCV},
  year={2016}
}

• Wei Wang, Yan Yan, +3 authors N. Sebe
• Published in ACCV 20 November 2016
• Computer Science

Action recognition has received increasing attention during the last decade. Various approaches have been proposed to encode the videos that contain actions, among which self-similarity matrices (SSMs) have shown very good performance by encoding the dynamics of the video. However, SSMs become sensitive when there is a very large view change. In this paper, we tackle the multi-view action recognition problem by proposing a sparse code filtering (SCF) framework which can mine the action patterns. First, a class-wise sparse coding method is proposed to make the sparse codes of the between-class data lie close by. Then we integrate the classifiers and the class-wise sparse coding process into a collaborative filtering (CF) framework to mine the discriminative sparse codes and classifiers jointly. The experimental results on several public multi-view action recognition datasets demonstrate that the presented SCF framework outperforms other state-of-the-art methods.