Equiangular kernel dictionary learning with applications to dynamic texture analysis

Yuhui Quan School of Computer Science & Engineering, South China Univ. of Tech., Guangzhou 510006, China; Department of Mathematics, National University of Singapore, Singapore 117542 Chenglong Bao Department of Mathematics, National University of Singapore, Singapore 117542 Hui Ji Department of Mathematics, National University of Singapore, Singapore 117542

Machine Learning mathscidoc:2206.41007

CVPR, 2016.4
Most existing dictionary learning algorithms consider a linear sparse model, which often cannot effectively characterize the nonlinear properties present in many types of visual data, e.g. dynamic texture (DT). Such nonlinear properties can be exploited by the so-called kernel sparse coding. This paper proposed an equiangular kernel dictionary learning method with optimal mutual coherence to exploit the nonlinear sparsity of high-dimensional visual data. Two main issues are addressed in the proposed method: (1) coding stability for redundant dictionary of infinite-dimensional space; and (2) computational efficiency for computing kernel matrix of training samples of high-dimensional data. The proposed kernel sparse coding method is applied to dynamic texture analysis with both local DT pattern extraction and global DT pattern characterization. The experimental results showed its performance gain over existing methods.
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@inproceedings{yuhui2016equiangular,
  title={Equiangular kernel dictionary learning with applications to dynamic texture analysis},
  author={Yuhui Quan, Chenglong Bao, and Hui Ji},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20220616153644364722379},
  booktitle={CVPR},
  year={2016},
}
Yuhui Quan, Chenglong Bao, and Hui Ji. Equiangular kernel dictionary learning with applications to dynamic texture analysis. 2016. In CVPR. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20220616153644364722379.
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