Simple and Robust Hard Cut Detection Using Interframe Differences

  • Alvaro Pardo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)


In this paper we introduce a simple method for the detection of hard cuts using only interframe differences. The method is inspired in the computational gestalt theory. The key idea in this theory is to define a meaningful event as large deviation from the expected background process. That is, an event that has little probability to occur given a probabilistic background model. In our case we will define a hard cut when the interframe differences have little probability to be produced by a given model of interframe differences of non-cut frames. Since we only use interframe differences, there is no need to perform motion estimation, or other type of processing, and the method turns to be very simple with low computational cost. The proposed method outperforms similar methods proposed in the literature.


Video Sequence Strong Motion Feature Tracking Shot Boundary 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.


  1. 1.
    Desolneux, A.: Evénements significatifs et applications à l’analyse d’images. PhD thesis, ENS-Cachan, France (2000)Google Scholar
  2. 2.
    Desolneux, A., Moisan, L., Morel, J.-M.: A grouping principle and four applications. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(4), 508–512 (2003)CrossRefGoogle Scholar
  3. 3.
    Desolneux, A., Moisan, L., Morel, J.-M.: Maximal meaningful events and applications to image analysis. The Annals of Statistics 31(6), 1822–1851 (2003)zbMATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Gargi, U., Kasturi, R., Strayer, S.H.: Performance characterization of video-shot-change detection methods. IEEE Transactions on Circuits and Systems for Video Technology (2000)Google Scholar
  5. 5.
    Hanjalic, A.: Shot-boundary detection: Unraveled and resolved. IEEE Transactions on Circuits and Systems for Video Technology (2002)Google Scholar
  6. 6.
    Huang, C.-L., Liao, B.Y.: A robust scene-change detection method for video segmentation. IEEE Transactions on Circuits and Systems for Video Technology (2001)Google Scholar
  7. 7.
    Pfeiffer, S., Leinhart, R., Kuhne, G., Effelserberg, W.: The MoCa Project - Movie Content Analysis REsearch at the University of MannheimGoogle Scholar
  8. 8.
    Whitehead, A., Bose, P., Laganiere, R.: Feature based cut detection with automatic threshold selection. In: Enser, P.G.B., Kompatsiaris, Y., O’Connor, N.E., Smeaton, A., Smeulders, A.W.M. (eds.) CIVR 2004. LNCS, vol. 3115, pp. 410–418. Springer, Heidelberg (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Alvaro Pardo
    • 1
    • 2
  1. 1.DIE, Facultad de Ingeniería y TecnologíasUniversidad Católica delUruguay
  2. 2.IIE, Facultad de IngenieríaUniversidad de la República 

Personalised recommendations