Shot Boundary Detection Using Multi-instance Incremental and Decremental One-Class Support Vector Machine

  • Hanhe Lin
  • Jeremiah D. Deng
  • Brendon J. Woodford
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9651)


This paper presents a novel framework to detect shot boundaries based on the One-Class Support Vector Machine (OCSVM). Instead of comparing the difference between pair-wise consecutive frames at a specific time, we measure the divergence between two OCSVM classifiers, which are learnt from two contextual sets, i.e., immediate past set and immediate future set. To speed up the processing procedure, the two OCSVM classifiers are updated in an online fashion by our proposed multi-instance incremental and decremental one-class support vector machine algorithm. Our approach, which inherits the advantages of OCSVM, is robust to noises such as abrupt illumination changes and large object or camera movements, and capable of detecting gradual transitions as well. Experimental results on some benchmark datasets compare favorably with the state-of-the-art methods.


Support vector machine One-class Kernel method Online learning Shot boundary detection 


  1. 1.
    Hanjalic, A.: Shot-boundary detection: unraveled and resolved? IEEE Trans. Circuits Syst. Video Technol. 12(2), 90–105 (2002)CrossRefGoogle Scholar
  2. 2.
    Huang, C.L., Liao, B.Y.: A robust scene-change detection method for video segmentation. IEEE Trans. Circuits Syst. Video Technol. 11(12), 1281–1288 (2001)CrossRefGoogle Scholar
  3. 3.
    Yuan, J., Wang, H., Xiao, L., Zheng, W., Li, J., Lin, F., Zhang, B.: A formal study of shot boundary detection. IEEE Trans. Circuits Syst. Video Technol. 17(2), 168–186 (2007)CrossRefGoogle Scholar
  4. 4.
    Lu, Z.M., Shi, Y.: Fast video shot boundary detection based on SVD and pattern matching. IEEE Trans. Image Process. 22(12), 5136–5145 (2013)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Kowdle, A., Chen, T.: Learning to segment a video to clips based on scene and camera motion. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part III. LNCS, vol. 7574, pp. 272–286. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  6. 6.
    Desobry, F., Davy, M., Doncarli, C.: An online kernel change detection algorithm. IEEE Trans. Signal Process. 53(8), 2961–2974 (2005)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Schölkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J., Williamson, R.C.: Estimating the support of a high-dimensional distribution. Neural Comput. 13(7), 1443–1471 (2001)CrossRefMATHGoogle Scholar
  8. 8.
    Swain, M.J., Ballard, D.H.: Color indexing. Int. J. Comput. Vis. 7(1), 11–32 (1991)CrossRefGoogle Scholar
  9. 9.
    Cauwenberghs, G., Poggio, T.: Incremental and decremental support vector machine learning. Adv. Neural Inf. Process. Syst. 13, 409–415 (2001)Google Scholar
  10. 10.
    Laskov, P., Gehl, C., Krüger, S., Müller, K.R.: Incremental support vector learning: analysis, implementation and applications. J.Mach. Learn. Res. 7, 1909–1936 (2006)MathSciNetMATHGoogle Scholar
  11. 11.
    Karasuyama, M., Takeuchi, I.: Multiple incremental decremental learning of support vector machines. In: Advances in Neural Information Processing Systems, pp. 907–915 (2009)Google Scholar
  12. 12.
    Golub, G.H., Van Loan, C.F.: Matrix Computations, vol. 3. JHU Press, Baltimore (2012)MATHGoogle Scholar
  13. 13.
    Mühling, M., Ewerth, R., Stadelmann, T., Zöfel, C., Shi, B., Freisleben, B.: University of Marburg at TRECVID 2007: Shot Boundary Detection and High Level Feature Extraction. In: TRECVID (2007)Google Scholar
  14. 14.
    Zhao, Z.C., Zeng, X., Liu, T., Cai, A.N.: BUPT at TRECVID 2007: Shot Boundary Detection. In: TRECVID (2007)Google Scholar
  15. 15.
    Ren, J., Jiang, J., Chen, J.: Determination of Shot Boundary in MPEG videos for TRECVID 2007. In: TRECVID (2007)Google Scholar
  16. 16.
    Kawai, Y., Sumiyoshi, H., Yagi, N.: Shot Boundary Detection at TRECVID 2007. In: TRECVID (2007)Google Scholar
  17. 17.
    Gönen, M., Alpaydın, E.: Multiple kernel learning algorithms. J. Mach. Learn. Res. 12, 2211–2268 (2011)MathSciNetMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Hanhe Lin
    • 1
  • Jeremiah D. Deng
    • 1
  • Brendon J. Woodford
    • 1
  1. 1.Department of Information ScienceUniversity of OtagoDunedinNew Zealand

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