Advertisement

Probabilistic Shot Boundary Detection Using Interframe Histogram Differences

  • Alvaro Pardo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4225)

Abstract

In this paper we study a method for the detection of shot boundaries using interframe histogram differences. Instead of using traditional distance between histograms, we use a probabilistic distance that indicates the chance of a given distance to be a shot boundary. We declare a shot change when the interframe histogram difference is a large deviation from the expected histogram interframe differences given past evidence. Like other histogram based methods the proposed one is very simple while being very robust and effective. The proposed method outperforms similar methods proposed in the literature for the detection of hard cuts and achieves good recall and precision performances for gradual transitions.

References

  1. 1.
    Hanjalic, A.: Shot-boundary detection: Unraveled and resolved. IEEE Transactions on Circuits and Systems for Video Technology (2002)Google Scholar
  2. 2.
    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
  3. 3.
    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
  4. 4.
    Whitehead, A., Bose, P., Laganiere., R.: Feature based cut detection with automatic threshold selection. In: Proceedings of the International Conference on Image and Video Retrieval, pp. 410–418 (2004)Google Scholar
  5. 5.
    Desolneux, A., Moisan, L., Morel, J.-M.: A grouping principle and four applications. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 508–512 (2003)CrossRefGoogle Scholar
  6. 6.
    Desolneux, A., Moisan, L., Morel, J.-M.: Maximal meaningful events and applications to image analysis. The Annals of Statistics 31, 1822–1851 (2003)zbMATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Desolneux, A.: Evénements significatifs et applications á l’analyse d’images. PhD thesis, ENS-Cachan, France (2000)Google Scholar
  8. 8.
    Pfeiffer, S., Leinhart, R., Kuhne, G., Effelserberg, W.: The MoCa Project - Movie Content Analysis REsearch at the University of Mannheim. In: Informatik 1998, pp. 329–338 (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

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

Personalised recommendations