Skip to main content
SpringerLink
Log in

Semi-supervised low-rank representation for image classification

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Low-rank representation (LRR) is a useful tool for seeking the lowest rank representation among all the coefficient matrices that represent the images as linear combinations of the basis in the given dictionary. However, it is an unsupervised method and has poor applicability and performance in real scenarios because of the lack of image information. In this paper, based on LRR, we propose a novel semi-supervised approach, called label constrained sparse low-rank representation (LCSLRR), which incorporates the label information as an additional hard constraint. Specifically, this paper develops an optimization process in which the improvement of the discriminating power of the low-rank decomposition is presented explicitly by adding the label information constraint. We construct LCSLRR-graph to represent data structures for semi-supervised learning and provide the weights of edges in the graph by seeking a low-rank and sparse matrix. We conduct extensive experiments on publicly available databases to verify the effectiveness of our novel algorithm in comparison to the state-of-the-art approaches through a set of evaluations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Liu, G., Lin, Z., Yu, Y.: Robust subspace segmentation by low-rank representation. Mach. Learn. pp. 663–670 (2010)

  2. Liu, G., Lin, Z., Yan, S., Sun, J., Yu, Y., Ma, Y.: Robust recovery of subspace structures by low-rank representation. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 171–184 (2013)

    Article  Google Scholar 

  3. Du, H., Hu, Q., Qiao, D., et al.: Robust face recognition via low-rank sparse representation-based classification. Int. J. Autom. Comput. 12(6), 579–587 (2015)

    Article  Google Scholar 

  4. Shen, X., Wu, Y.: A unified approach to salient object detection via low rank matrix recovery. IEEE Conference on Computer Vision and Pattern Recognition pp. 853–860 (2012)

  5. Cui, X., Huang, J., Zhang, S., Metaxas, D.: Background Subtraction Using Low Rank and Group Sparsity Constraints. Springer, Berlin (2012)

    Book  Google Scholar 

  6. Zhang, T., Ghanem, B., Ahuja, N.: Low-rank Sparse Learning for Robust Visual Tracking. Springer, Berlin (2012)

    Book  Google Scholar 

  7. Lee, J., Shi, B. ,Matsushita, Y., Kweon, I., Ikeuchi, K.: Radiometric calibration by transform invariant low-rank structure. IEEE Conference on Computer Vision and Pattern Recognition, pp. 2337–2344 (2011)

  8. He, R., Zheng, W.S., Hu, B.G., Kong, X.W.: Nonnegative sparse coding for discriminative semi-supervised learning. IEEE Conference on Computer Vision and Pattern Recognition, pp. 792–801 (2011)

  9. Fumin, S., Chunhua, S., Anton, H., Zhenmin, T.: Approximate least trimmed sum of squares fitting and applications in image analysis. IEEE Trans. Image Process. 22(5), 1836–1847 (2013)

    Article  MathSciNet  Google Scholar 

  10. Fumin, S., Chunhua, S., Rhys, H., Anton, H., Zhenmin, T.: Fast approximate \(L_\infty \) minimization: speeding up robust regression. Comput. Stat. Data Anal. 77, 25–37 (2014)

    Article  MathSciNet  Google Scholar 

  11. Fumin, S., Wankou, Y., et al.: Robust regression based face recognition with fast outlier removal. Multimed. Tools Appl. 1–12 (2014)

  12. Zhuang, L., Gao, H., Lin, Z., Ma, Y., Zhang, X., Yu, N.: Non-negative low rank and sparse graph for semi-supervised learning. IEEE Conference on Computer Vision and Pattern Recognition pp. 2328–2335 (2012)

  13. Ma, L., Wang, C., Xiao, B., Zhou, W.: Sparse representation for face recognition based on discriminative low-rank dictionary learning. IEEE Conference on Computer Vision and Pattern Recognition, pp. 2586–2593 (2012)

  14. Zhang, Y., Jiang, Z., Davis, L.S.: Learning structured low-rank representations for image classification. IEEE Conference on Computer Vision and Pattern Recognition, pp. 676–683 (2013)

  15. Candés, E.J., Li, X., Ma, Y., Wright, J.: Robust principal component analysis? JACM 58(3) (2011)

  16. Cai, J.F., Candés, E.J., Shen, Z.: A singular value thresholding algorithm for matrix completion. SIAM J. Optim. 4, 1956–1982 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  17. Bertsekas, D.P.: Constrained Optimization and Lagrange Multiplier Methods. Computer Science and Applied Mathematics, vol. 1, pp. 234–243. Academic Press, Boston (1982)

    Google Scholar 

  18. Lin, Z., Liu, R., Su, Z.: Linearized alternating direction method with adaptive penalty for low rank representation. In NIPS (2011)

  19. http://cvc.yale.edu/projects/yalefacesB/yalefacesB.html

  20. http://www.ri.cmu.edu/projects/$project_418$.html

  21. http://www.gaussianprocess.org/gpml/data/

  22. http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html

  23. http://www2.ece.ohio-state.edu/~aleix/ARdatabase.html

  24. http://sparselab.stanford.edu/

  25. Zhu, X., Ghahramani, Z., Lafferty, J.: Semi-supervised learning using Gaussian fields and harmonic functions. ICML 20, 912–919 (2003)

    Google Scholar 

  26. Zhou, D., Bousquet, O., Lal, T., Weston, J., Schölkopf, B.: Learning with local and global consistency. In NIPS, pp. 595–602 (2004)

  27. Yan, S., Wang, H.: Semi-supervised learning by sparse representation, pp. 792–801. In SIAM International Conference on Data Mining, SDM (2009)

  28. Wright, J., Ma, Y., Mairal, J., Sapiro, G., Huang, T.S., Yan, S.: Sparse representation for computer vision and pattern recognition. Proc. IEEE 98(6), 1031–1044 (2010)

    Article  Google Scholar 

  29. Cai, D., He, X., Han, J.: Semi-supervised discriminant analysis. IEEE 11th International Conference on Computer Vision, pp. 1–7 (2007)

  30. Li, Y., Liu, J., Lu, H., et al.: Learning robust face representation with classwise block-diagonal structure. IEEE Trans. Inf. Forensics Secur. 9(12), 2051–2062 (2014)

    Article  Google Scholar 

  31. Yang, S., Feng, Z., Ren, Y., et al.: Semi-supervised classification via kernel low-rank representation graph. Knowledge-Based Syst. 69, 150–158 (2014)

    Article  Google Scholar 

  32. Yang, S., Wang, X., Wang, M., et al.: Semi-supervised low-rank representation graph for pattern recognition. IET Image Process. 7(2), 131–136 (2013)

    Article  MathSciNet  Google Scholar 

  33. Chen, C. F., Wei, C. P., Wang, Y. C. F.: Low-rank matrix recovery with structural incoherence for robust face recognition. Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on IEEE, pp. 2618–2625 (2012)

  34. Wright, J., Ganesh, A., Rao, S., et al.: Robust principal component analysis: exact recovery of corrupted low-rank matrices via convex optimization. Advances in neural information processing systems, pp. 2080–2088 (2009)

  35. Wright, J., Yang, A.Y., Ganesh, A., et al.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 210–227 (2009)

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (61375038), Applied Basic Research Programs of Sichuan Science and Technology Department (2016JY0088), National Natural Science Foundation of China (11401060), Zhejiang Provincial Natural Science Foundation of China (LQ13 A010023).

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Center for Robotics, Key Laboratory for NeuroInformation of Ministry of Education, University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China

    Chenxue Yang, Mao Ye, Song Tang & Tao Xiang

  2. School of Science, Chongqingjiaotong University, Chongqing, 400074, People’s Republic of China

    Zijian Liu

Authors
  1. Chenxue Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mao Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Song Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tao Xiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zijian Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mao Ye.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, C., Ye, M., Tang, S. et al. Semi-supervised low-rank representation for image classification. SIViP 11, 73–80 (2017). https://doi.org/10.1007/s11760-016-0895-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-016-0895-4

Keywords

Search

Navigation