Skip to main content
Springer Nature Link
Log in

Unsupervised kernel learning for abnormal events detection

  • Original Article
  • Published:
The Visual Computer Aims and scope Submit manuscript

Abstract

In this paper, we propose a method to detect abnormal events using a novel unsupervised kernel learning algorithm. The key of our method is to learn a suitable feature space and the associated kernel function of the training samples. By considering the self-similarity property of training samples, we assume that the training samples will show the distinctly clustering property in the obtained feature space. Non-negative matrix factorization (NMF) is used to learn the feature space, and the support vector data description (SVDD) method is adopted to measure the clustering degree of instances in the feature space. We append the clustering constraints in the process of learning the feature space and use the bases produced by NMF as the projection matrix to construct the kernel function in SVDD. In other words, we incorporate the minimal enclosing sphere constraints within the NMF formulation. In the process of feature space learning, instances in the obtained feature space will be described better and better by an hypersphere. Our algorithm converges to a local optimal solution by applying an alternating optimization approach. Experimental results on three public datasets and the comparison to the state-of-the-art methods show that our method is effective in detecting and locating unknown abnormal behaviors.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401, 788–791 (1999)

    Article  Google Scholar 

  2. Lee, D.D., Seung, H.S.: Algorithms for non-negative matrix factorization. NIPS 13, 629–634 (2001)

    Google Scholar 

  3. Thurau, C., Hlavac, V.: Pose primitive based human action recognition in videos or still images, CVPR, pp. 1–8 (2008)

  4. Tax, D.M.J., Duin, R.P.W.: Support vector data description. Mach. Learn. 54, 45–66 (2004)

    Article  MATH  Google Scholar 

  5. Christoph, H., Lampert, C.H.: Kernel methods in computer vision. Found. Trends Comput. Graph. Vis. 4(3), 193–285 (2009)

    Google Scholar 

  6. Cong, Y., Yuan, J., Liu, J.: Sparse reconstruction cost for abnormal event detection. CVPR, pp. 3449–3456 (2011)

  7. Wu, S., Moore, B., Shah, M.: Chaotic invariants of Lagrangian particle trajectories for anomaly detection in crowded scenes. In: CVPR (2010)

  8. Vijay Kumar, B.G., et al.: Max-margin non-negative matrix factorization. Image Vis. Comput. 30, 279–291 (2012)

    Article  Google Scholar 

  9. Ding, C.H.Q., Li, T., Jordan, M.I.: Convex and semi-nonnegative matrix factorizations. IEEE Trans. Pattern Anal. Mach. Intell. 32(1), 45–55 (2010)

    Google Scholar 

  10. Ritter, G., Gallegos, M.T.: Outliers in statistical pattern recognition and an application to automatic chromosome classification. Pattern Recognit. Lett. 18, 525–539 (1997)

    Article  Google Scholar 

  11. Pers, J., et al.: Histograms of optical flow for efficient representation of body motion. Pattern Recognit. Lett. 31, 1369–1376 (2010)

    Article  Google Scholar 

  12. http://www.svcl.ucsd.edu/projects/anomaly

  13. Smola, A.: Learning: with Kernels: support vector machines. MIT Press, Cambridge, MA (2002)

    Google Scholar 

  14. Yuan, J., Liu, Z., Wu, Y.: Discriminative subvolume search for efficient action detection. CVPR, pp. 2442–2449 (2009)

  15. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. CVPR, pp. 886–893 (2005)

  16. Cong, Y., Gong, H., Zhu, S., Tang, Y.: Flow mosaicking: real-time pedestrian counting without scene-specific learning. CVPR, pp. 1093–1100 (2009)

  17. Kim, J., Grauman, K.: Observe locally, infer globally: a space-time MRF for detecting abnormal activities with incremental up-dates. In: CVPR (2009)

  18. Kratz, L., Nishino, K.: Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models. In: CVPR (2009)

  19. Ramin Mehran, M.S., Oyama, A.: Abnormal crowd behavior detection using social force model. In: CVPR (2009)

  20. Beibei Zhan, P.R.S.V., Monekosso, D., Xu, L.-Q.: Crowd analysis: a survey. Mach. Vis. Appl. 19, 345–357 (2008)

    Google Scholar 

  21. Mahadevan, V., Li, W., Bhalodia, V., Vasconcelos, N.: Anomaly detection in crowded scenes. CVPR, pp. 1975–1981 (2010)

  22. Pascual-Montano, J., Carazo, K., Kochi, D., Lehmann, R.D.: Pascual-Marqui, Nonsmooth nonnegative matrix factorization (NSNMF). IEEE Trans. Pattern Anal. Mach. Intell. 28(3), 403–415 (2006)

    Article  Google Scholar 

  23. Zhang, D., Zhou, Z., Chen, S.: Non-negative matrix factorization on kernels, PRICAI. pp. 404–412 (2006)

  24. Bociu, I., Pitas, I.: A new sparse image representation algorithm applied to facial expression recognition, machine learning for signal processing, 2004. Proceedings of the 2004 14th IEEE signal processing society workshop, pp. 539–548 (2004)

  25. Wasserman, L.: All of statistics: a concise course in statistical inference. (2004)

  26. http://mha.cs.umn.edu/movies/crowdactivity-all.avi

  27. Tropp, J.: Literature survey: non-negative matrix factorization, university of Texas at Austin, preprint (2003)

  28. Singh, K., Upadhyaya, S.: Outlier detection: applications and techniques. Int. J. Comput. Sci. Issues 9(1), 3 (2012)

    Google Scholar 

  29. Han, J., Kamber. M.: Data Mining: Concepts and Techniques, 2nd edn. Elsevier, Amsterdam, pp. 451–460

  30. Fan, H., Zaïane, O. R., Foss, A., Wu, J.: A nonparametric outlier detection for efficiently discovering top-n outliers from engineering data. In: Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Singapore (2006)

  31. Jin, W., Tung, A., Han, J., and Wang, W. Ranking outliers using symmetric neighborhood relationship. In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Singapore (2006)

  32. Kriegel, H.-P., Schubert, M., Zimek, A.: Angle-based outlier detection. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD), Las Vegas, NV (2008)

  33. Chapelle, O., Zien, A.: Semi-supervised classification by low density separation. In: Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics, pp. 57–64 (2005)

  34. McLachlan, G.J., Do, K.A., Ambroise, C.: Analyzing microarray gene expression data. Wiley, London (2004)

    Book  MATH  Google Scholar 

Download references

Acknowledgments

This paper is supported by College of Information System and Management, National University of Defense Technology and subsidized by National Natural Science Foundation of China(Grant No. 61170158).

Author information

Authors and Affiliations

  1. College of Information System and Management, National University of Defense Technology, Changsha,  410072, People’s Republic of China

    Weiya Ren, Guohui Li, Boliang Sun & Kuihua Huang

Authors
  1. Weiya Ren
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Guohui Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Boliang Sun
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Kuihua Huang
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Weiya Ren.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ren, W., Li, G., Sun, B. et al. Unsupervised kernel learning for abnormal events detection. Vis Comput 31, 245–255 (2015). https://doi.org/10.1007/s00371-013-0915-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00371-013-0915-0

Keywords