International Conference on Multimedia Modeling

MultiMedia Modeling pp 3-15 | Cite as

Video Event Detection Using Kernel Support Vector Machine with Isotropic Gaussian Sample Uncertainty (KSVM-iGSU)

  • Christos Tzelepis
  • Vasileios Mezaris
  • Ioannis Patras
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9516)

Abstract

In this paper, we propose an algorithm that learns from uncertain data and exploits related videos for the problem of event detection; related videos are those that are closely associated, though not fully depicting the event of interest. In particular, two extensions of the linear SVM with Gaussian Sample Uncertainty are presented, which (a) lead to non-linear decision boundaries and (b) incorporate related class samples in the optimization problem. The resulting learning methods are especially useful in problems where only a limited number of positive and related training observations are provided, e.g., for the 10Ex subtask of TRECVID MED, where only ten positive and five related samples are provided for the training of a complex event detector. Experimental results on the TRECVID MED 2014 dataset verify the effectiveness of the proposed methods.

Keywords

Video event detection Very few positive samples Related samples Learning with uncertainty Kernel methods Relevance degree SVMs 

Notes

Acknowledgment

This work was supported by the European Commission under contract FP7-600826 ForgetIT.

References

  1. 1.
    Bhattacharyya, C., Pannagadatta, K., Smola, A.J.: A second order cone programming formulation for classifying missing data. In: Neural Information Processing Systems (NIPS), pp. 153–160 (2005)Google Scholar
  2. 2.
    Bolles, R., Burns, B., Herson, J., et al.: The 2014 SESAME multimedia event detection and recounting system. In: Proceedings of the TRECVID Workshop (2014)Google Scholar
  3. 3.
    Broyden, C.G.: The convergence of a class of double-rank minimization algorithms 1. general considerations. IMA J. Appl. Math. 6(1), 76–90 (1970)MATHMathSciNetCrossRefGoogle Scholar
  4. 4.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:1–27:27 (2011). http://www.csie.ntu.edu.tw/cjlin/libsvm CrossRefGoogle Scholar
  5. 5.
    Cheng, H., Liu, J., Chakraborty, I., Chen, G., Liu, Q., Elhoseiny, M., Gan, G., Divakaran, A., Sawhney, H., Allan, J., Foley, J., Shah, M., Dehghan, A., Witbrock, M., Curtis, J.: SRI-Sarnoff AURORA system at TRECVID 2014 multimedia event detection and recounting. In: Proceedings of the TRECVID Workshop (2014)Google Scholar
  6. 6.
    Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition CVPR 2009, pp. 248–255. IEEE (2009)Google Scholar
  7. 7.
    Douze, M., Oneata, D., Paulin, M., Leray, C., Chesneau, N., Potapov, D., Verbeek, J., Alahari, K., Harchaoui, Z., Lamel, L., Gauvain, J.L., Schmidt, C.A., Schmid, C.: The INRIA-LIM-VocR and AXES submissions to TRECVID 2014 multimedia event detection (2014)Google Scholar
  8. 8.
    Gkalelis, N., Markatopoulou, F., Moumtzidou, A., Galanopoulos, D., Avgerinakis, K., Pittaras, N., Vrochidis, S., Mezaris, V., Kompatsiaris, I., Patras, I.: ITI-CERTH participation to TRECVID 2014. In: Proceedings of the TRECVID Workshop (2014)Google Scholar
  9. 9.
    Gkalelis, N., Mezaris, V.: Video event detection using generalized subclass discriminant analysis and linear support vector machines. In: Proceedings of International Conference on Multimedia Retrieval, p. 25. ACM (2014)Google Scholar
  10. 10.
    Golub, G.H., Van Loan, C.F.: Matrix Comput., vol. 3. JHU Press, Baltimore (2012)Google Scholar
  11. 11.
    Guangnan, Y., Dong, L., Shih-Fu, C., Ruslan, S., Vlad, M., Larry, D., Abhinav, G., Ismail, H., Sadiye, G., Ashutosh, M.: BBN VISER TRECVID 2014 multimedia event detection and multimedia event recounting systems. In: Proceedings of the TRECVID Workshop (2014)Google Scholar
  12. 12.
    Habibian, A., van de Sande, K.E., Snoek, C.G.: Recommendations for video event recognition using concept vocabularies. In: Proceedings of the 3rd ACM Conference on International Conference on Multimedia Retrieval, pp. 89–96. ACM (2013)Google Scholar
  13. 13.
    Habibian, A., Mensink, T., Snoek, C.G.: Videostory: A new multimedia embedding for few-example recognition and translation of events. In: Proceedings of the ACM International Conference on Multimedia, pp. 17–26. ACM (2014)Google Scholar
  14. 14.
    Jiang, L., Meng, D., Mitamura, T., Hauptmann, A.G.: Easy samples first: self-paced reranking for zero-example multimedia search. In: Proceedings of the ACM International Conference on Multimedia, pp. 547–556. ACM (2014)Google Scholar
  15. 15.
    Jiang, L., Yu, S.I., Meng, D., Mitamura, T., Hauptmann, A.G.: Bridging the ultimate semantic gap: a semantic search engine for internet videos. In: ACM International Conference on Multimedia Retrieval (2015)Google Scholar
  16. 16.
    Jiang, Y.G., Bhattacharya, S., Chang, S.F., Shah, M.: High-level event recognition in unconstrained videos. Int. J. Multimedia Inf. Retrieval 2(2), 73–101 (2013)CrossRefGoogle Scholar
  17. 17.
    Kimeldorf, G., Wahba, G.: Some results on Tchebycheffian spline functions. J. Math. Anal. Appl. 33(1), 82–95 (1971)MATHMathSciNetCrossRefGoogle Scholar
  18. 18.
    Lanckriet, G.R., Ghaoui, L.E., Bhattacharyya, C., Jordan, M.I.: A robust minimax approach to classification. J. Mach. Learn. Res. 3, 555–582 (2003)MATHMathSciNetGoogle Scholar
  19. 19.
    Liang, Z., Inoue, N., Shinoda, K.: Event Detection by Velocity Pyramid. In: Gurrin, C., Hopfgartner, F., Hurst, W., Johansen, H., Lee, H., O’Connor, N. (eds.) MMM 2014, Part I. LNCS, vol. 8325, pp. 353–364. Springer, Heidelberg (2014) CrossRefGoogle Scholar
  20. 20.
    Liu, D.C., Nocedal, J.: On the limited memory BFGS method for large scale optimization. Mathematical prog. 45(1–3), 503–528 (1989)MATHMathSciNetCrossRefGoogle Scholar
  21. 21.
    Mazloom, M., Habibian, A., Liu, D., Snoek, C.G., Chang, S.F.: Encoding concept prototypes for video event detection and summarization (2015)Google Scholar
  22. 22.
    Over, P., Awad, G., Michel, M., Fiscus, J., Sanders, G., Kraaij, W., Smeaton, A.F., Quenot, G.: An overview of the goals, tasks, data, evaluation mechanisms and metrics. In: Proceedings of the TRECVID 2014. NIST, USA (2014)Google Scholar
  23. 23.
    Robertson, S.: A new interpretation of average precision. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 689–690. ACM (2008)Google Scholar
  24. 24.
    Schölkopf, B., Herbrich, R., Smola, A.J.: A generalized representer theorem. In: Helmbold, D.P., Williamson, B. (eds.) COLT 2001 and EuroCOLT 2001. LNCS (LNAI), vol. 2111, pp. 416–426. Springer, Heidelberg (2001)Google Scholar
  25. 25.
    Shivaswamy, P.K., Bhattacharyya, C., Smola, A.J.: Second order cone programming approaches for handling missing and uncertain data. J. Mach. Learn. Res. 7, 1283–1314 (2006)MATHMathSciNetGoogle Scholar
  26. 26.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv preprint arXiv:1409.1556
  27. 27.
    Tzelepis, C., Mezaris, V., Patras, I.: Linear maximum margin classifier for learning from uncertain data (2015). arXiv preprint arXiv:1504.03892
  28. 28.
    Tzelepis, C., Gkalelis, N., Mezaris, V., Kompatsiaris, I.: Improving event detection using related videos and relevance degree support vector machines. In: Proceedings of the 21st ACM International Conference on Multimedia, pp. 673–676. ACM (2013)Google Scholar
  29. 29.
    Xu, H., Caramanis, C., Mannor, S.: Robustness and regularization of support vector machines. J. Mach. Learn. Res. 10, 1485–1510 (2009)MATHMathSciNetGoogle Scholar
  30. 30.
    Xu, H., Mannor, S.: Robustness and generalization. Mach. Learn. 86(3), 391–423 (2012)MATHMathSciNetCrossRefGoogle Scholar
  31. 31.
    Yu, S.I., Jiang, L., Mao, Z., Chang, X., Du, X., Gan, C., Lan, Z., Xu, Z., Li, X., Cai, Y., et al.: Informedia at TRECVID 2014 MED and MER. In: NIST TRECVID Video Retrieval Evaluation Workshop (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Christos Tzelepis
    • 1
    • 2
  • Vasileios Mezaris
    • 1
  • Ioannis Patras
    • 2
  1. 1.Information Technologies Institute (ITI), CERTHThermiGreece
  2. 2.Queen Mary University of LondonLondonUK

Personalised recommendations