$p$-Laplacian Regularized Sparse Coding for Human Activity Recognition

@article{Liu2016pLaplacianRS,
  title={\$p\$-Laplacian Regularized Sparse Coding for Human Activity Recognition},
  author={Weifeng Liu and Zheng-Jun Zha and Yanjiang Wang and Ke Lu and Dacheng Tao},
  journal={IEEE Transactions on Industrial Electronics},
  year={2016},
  volume={63},
  pages={5120-5129}
}
Human activity analysis in videos has increasingly attracted attention in computer vision research with the massive number of videos now accessible online. Although many recognition algorithms have been reported recently, activity representation is challenging. Recently, manifold regularized sparse coding has obtained promising performance in action recognition, because it simultaneously learns the sparse representation and preserves the manifold structure. In this paper, we propose a… CONTINUE READING
Highly Cited
This paper has 67 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 37 extracted citations

Learning a General Assignment Model for Video Analytics

IEEE Transactions on Circuits and Systems for Video Technology • 2018
View 4 Excerpts
Highly Influenced

68 Citations

020402016201720182019
Citations per Year
Semantic Scholar estimates that this publication has 68 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-10 of 81 references

A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems

SIAM J. Imaging Sciences • 2009
View 3 Excerpts
Highly Influenced

Regularization on Discrete Spaces

DAGM-Symposium • 2005
View 4 Excerpts
Highly Influenced

Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision • 2004
View 4 Excerpts
Highly Influenced

An Efficient Tracking System by Orthogonalized Templates

IEEE Transactions on Industrial Electronics • 2016

Classification with Noisy Labels by Importance Reweighting

IEEE Transactions on Pattern Analysis and Machine Intelligence • 2016
View 1 Excerpt

Manifold Ranking-Based Matrix Factorization for Saliency Detection

IEEE Transactions on Neural Networks and Learning Systems • 2016
View 1 Excerpt

Similar Papers

Loading similar papers…