Automatic Video Shot Boundary Detection Using Machine Learning

  • Wei Ren
  • Sameer Singh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3177)


In this paper we present a machine learning system that can accurately predict the transitions between frames in a video sequence. We propose a set of novel features and describe how to use dominant features based on a coarse-to-fine strategy to accurately predict video transitions.


Successive Frame Video Shot Video Segmentation Video Indexing Shot Boundary Detection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Alattar, A.M.: Detecting fade regions in uncompressed video sequences. In: Proc. IEEE. ICASSP 1997, pp. 3025–3028 (1997)Google Scholar
  2. 2.
    Boccignone, G., de Santo, M., Percanella, G.: An algorithm for video cut detection in Mpeg sequences. In: Proc. SPIE, Storage and Retrieval for Media Databases, San Jose, CA (2000)Google Scholar
  3. 3.
    Boresczky, S., Rowe, L.A.: A comparison of video shot boundary detection techniques. Proc. SPIE 2664, 170–179 (1996)CrossRefGoogle Scholar
  4. 4.
    Boreczky, J.S., Wilcox, L.D.: A Hidden Markov Model framework for video segmentation using audio and image features. In: Proceedings of ICASSP 1998, Seattle, May 1998, pp. 3741–3744 (1998)Google Scholar
  5. 5.
    Brunelli, R., Mich, O., Modena, C.M.: A survey on video indexing, IRST-Technical report 9612-06 (1996)Google Scholar
  6. 6.
    Dailianas, A., Allen, R.B., England, P.: Comparison of automatic video segmentation algorithms. Proc. SPIE Photonics West 2615, 2–16 (1995)CrossRefGoogle Scholar
  7. 7.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern classification. Wiley, Chichester (2001)zbMATHGoogle Scholar
  8. 8.
    Fernando, W.A.C., Canagarajah, C.N., Bull, D.R.: Fade and dissolve detection in uncompressed and compressed video sequence. In: Proc. ICIP Conference, pp. 299–303 (1999)Google Scholar
  9. 9.
    Gargi, U., Kasturi, R., Antani, S.: Performance characterization and comparison of video indexing algorithms. In: Proc. IEEE CVPR, pp. 559–565 (1998)Google Scholar
  10. 10.
    Jolion, J.M.: Feature similarity. In: Lew, M.S. (ed.) Principles of Visual Information Retrieval, Springer, Heidelberg (2001)Google Scholar
  11. 11.
    Kobla, V., Dementhon, D., Doermann, D.: Special effect edit detection using Video-Trails: a comparison with existing techniques. In: Proc. SPIE, pp. 302–310 (1999)Google Scholar
  12. 12.
    Koprinska, Carrato, S.: Video segmentation- a survey. Signal Processing: Image Communication 16(5), 477–500 (2001)CrossRefGoogle Scholar
  13. 13.
    Lienhart, R.: Comparison of automatic shot boundary detection algorithms. In: Proceedings of SPIE, pp. 3656–3659 (1999)Google Scholar
  14. 14.
    Lienhart, R.: Reliable Transition Detection in Videos: A survey and practitioner’s guide. International Journal of Image and Graphics 1, 469–486 (2001)CrossRefGoogle Scholar
  15. 15.
    Lienhart, R., Zaccarin, A.: A system for reliable dissolve detection in videos. In: Proc. IEEE ICIP Conference, Thessaloniki (2001)Google Scholar
  16. 16.
    Meng, J., Juan, Y., Chang, S.F.: Scene change detection in a MPEG compressed video sequence. In: Proc. IS&T/SPIE Symposium. SPIE, vol. 2419, pp. 14–25 (1995)Google Scholar
  17. 17.
    Nagasaka, A., Tanaka, Y.: Automatic video indexing and full-video search for object appearances. In: Proc. of IFIP TC2/WG2.6, pp. 113–127 (1991)Google Scholar
  18. 18.
    Pass, G., Zabih, R., Miller, J.: Comparing images using colour coherence vectors. In: Proc. Of the Fourth ACM Multimedia Conference, pp. 65–73 (1996)Google Scholar
  19. 19.
    Puzicha, J., Rubner, Y., Tomasi, C., Buhmann, J.M.: Empirical Evaluation of Dissimilarity Measures for Color and Texture. In: IEEE ICCV, Greece, pp. 1165–1172 (1999)Google Scholar
  20. 20.
    Ren, W., Singh, M., Singh, S.: Automated video segmentation. In: Proc. 3rd International Conference on Information, Communications & Signal Processing (2001)Google Scholar
  21. 21.
    Rubner, Y., Tomasi, C., Guibas, L.J.: The Earth Mover’s Distance as a metric for image retrieval. IJCV Journal, 99–121 (2000)Google Scholar
  22. 22.
    Sethi, I.K., Patel, N.: A statistical approach to scene change detection. SPIE 2420, 329–339 (1995)CrossRefGoogle Scholar
  23. 23.
    Song, H.S., Kim, I.K., Cho, N.I.: Scene change detection by feature extraction from strong edge blocks. Proc. of SPIE 4671, 484–492 (2002)Google Scholar
  24. 24.
    Truong, B.T., Dorai, C., Venkatesh, S.: New enhancements to cut, fade, and dissolve detection in video segmentation. In: ACM Multimedia 2000, pp. 219–227 (2000)Google Scholar
  25. 25.
    Yeo, B.L., Liu, B.: Rapid scene analysis on compressed video. IEEE Transactions on Circuits and Systems for Video Technology 5, 533–544 (1995)CrossRefGoogle Scholar
  26. 26.
    Yeo, B.L., Liu, B.: A unified approach to temporal segmentation of motion JPEG and MPEG compressed video. In: Proc. IEEE ICMCS, pp. 81–88 (1999b)Google Scholar
  27. 27.
    Webb, A.: Statistical Pattern Recognition. Arnold, London (1999)zbMATHGoogle Scholar
  28. 28.
    Yusoff, Y., Christmas, W., Kittler, J.: Video shot cut detection using adaptive thresholding. In: Proc. British Machine Vision Conference (2000)Google Scholar
  29. 29.
    Yusoff, Y., Christmas, W., Kittler, J.: A study on automatic shot change detection. In: ECMAST 1998. LNCS, vol. 1425, pp. 177–189. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  30. 30.
    Yu, J., Srinath, M.D.: An efficient method for scene cut detection. Pattern Recognition Letters 22, 1379–1391 (2001)zbMATHCrossRefGoogle Scholar
  31. 31.
    Zabih, R., Miller, J., Mai, K.: A feature-based algorithm for detecting and classifying scene breaks. In: Proc. ACM Multimedia, pp. 189–200 (1995)Google Scholar
  32. 32.
    Zabih, R., Miller, J., Mai, K.: A feature-based algorithm for detecting and classification production effects. Multimedia Systems 7, 119–128 (1999)CrossRefGoogle Scholar
  33. 33.
    Zhang, J., Kankanhalli, A., Smoliar, S.W.: Automatic partitioning of full-motion video. Multimedia Systems 1, 10–28 (1993)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Wei Ren
    • 1
  • Sameer Singh
    • 1
  1. 1.ATR Lab, Department of Computer ScienceUniversity of ExeterExeterUK

Personalised recommendations