Reducing False Positives in Video Shot Detection Using Learning Techniques

  • Nithya Manickam
  • Aman Parnami
  • Sharat Chandran
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4338)


Video has become an interactive medium of daily use today. However, the sheer volume of the data makes it extremely difficult to browse and find required information. Organizing the video and locating required information effectively and efficiently presents a great challenge to the video retrieval community. This demands a tool which would break down the video into smaller and manageable units called shots.

Traditional shot detection methods use pixel difference, histograms, or temporal slice analysis to detect hard-cuts and gradual transitions. However, systems need to be robust to sequences that contain dramatic illumination changes, shaky camera effects, and special effects such as fire, explosion, and synthetic screen split manipulations. Traditional systems produce false positives for these cases; i.e., they claim a shot break when there is none.

We propose a shot detection system which reduces false positives even if all the above effects are cumulatively present in one sequence. Similarities between successive frames are computed by finding the correlation and is further analyzed using a wavelet transformation. A final filtering step is to use a trained Support Vector Machine (SVM). As a result, we achieve better accuracy (while retaining speed) in detecting shot-breaks when compared with other techniques.


False Positive Gradual Transition Illumination Change Morlet Wavelet Successive Frame 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Zhang, H., Kankanhalli, A., Smoliar, S.: Automatic partitioning of full-motion video. ACM Multimedia Systems 1, 10–28 (1993)CrossRefGoogle Scholar
  2. 2.
    Bang, C., Chenl, S.C., Shyu, M.L.: Pixso: a system for video shot detection. In: Fourth International Conference on Information, Communications and Signal Processing, pp. 1320–1324 (2003)Google Scholar
  3. 3.
    Shahraray, S.: Scene change detection and content-based sampling of video sequence. In: SPIE Storage and Retrieval for Image and Video Databases, pp. 2–13 (1995)Google Scholar
  4. 4.
    Swanberg, D., Shu, C., Jain, R.: Knowledge guided parsing in video database. In: SPIE Storage and Retrieval for Image and Video Databases, pp. 13–24 (1993)Google Scholar
  5. 5.
    Funt, B., Finlayson, G.: Color constant color indexing. Pattern Analysis and Machine Intelligence, IEEE 17, 522–529 (1995)CrossRefGoogle Scholar
  6. 6.
    Rasheed, Z., Shah, M.: Scene detection in Hollywood movies and TV shows. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–348 (2003)Google Scholar
  7. 7.
    Patel, N., Sethi, I.: Video shot detection and characterization for video databases. Pattern Recognition 30, 583–592 (1997)CrossRefGoogle Scholar
  8. 8.
    Li, D., Lu, H.: Avoiding false alarms due to illumination variation in shot detection. In: IEEE Workshop on Signal Processing Systems, pp. 828–836 (2000)Google Scholar
  9. 9.
    Lu, H., Tan, Y.: An effective post-refinement method for shot boundary detection. CirSysVideo 15, 1407–1421 (2005)Google Scholar
  10. 10.
    Zabih, R., Miller, J., Mai, K.: Feature-based algorithms for detecting and classifying scene breaks. Technical report, Cornell University (1995)Google Scholar
  11. 11.
    Yuliang, G., De, X.: A solution to illumination variation problem in shot detection. In: TENCON 2004. IEEE Region 10 Conference, pp. 81–84 (2004)Google Scholar
  12. 12.
    Ngo, C., Pong, T., Chin, R.: Detection of gradual transitions through temporal slice analysis. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 36–41 (1999)Google Scholar
  13. 13.
    Yeo, C., Zhu, Y.W., Sun, Q., Chang, S.F.: A framework for sub-window shot detection. In: MMM 2005: Eleventh International Multimedia Modelling Conference (MMM 2005), pp. 84–91 (2005)Google Scholar
  14. 14.
    Amir, A., et al.: IBM Research TRECVID-2005 Video Retrieval System. In: TREC Proc. (2005)Google Scholar
  15. 15.
    Vlachos, T.: Cut detection in video sequences using phase correlation. Signal Processing Letters 7, 173–175 (2000)CrossRefGoogle Scholar
  16. 16.
    Yoo, H.W., Ryoo, H.J., Jang, D.S.: Gradual shot boundary detection using localized edge blocks. Multimedia Tools and Applications 28, 283–300 (2006)CrossRefGoogle Scholar
  17. 17.
    Petersohn, C.: Fraunhofer HHI at TRECVID 2004: Shot boundary detection system. In: TREC Proc. (2004)Google Scholar
  18. 18.
    Covell, M., Ahmad, S.: Analysis by synthesis dissolve detection. In: International Conference on Image Processing, pp. 425–428 (2002)Google Scholar
  19. 19.
    Lienhart, R., Zaccarin, A.: A system for reliable dissolve detection in videos. In: International Conference on Image Processing, pp. 406–409 (2001)Google Scholar
  20. 20.
    NIST: TREC Video Retrieval Evaluation (2005),

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Nithya Manickam
    • 1
  • Aman Parnami
    • 1
  • Sharat Chandran
    • 1
  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology Bombay 

Personalised recommendations