On the Use of Entropy Series for Fade Detection

  • José San Pedro
  • Sergio Domínguez
  • Nicolas Denis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4177)


Accurate shot boundary detection techniques have been an important research topic in the last decade. Such interest is motivated by the fact that segmentation of a video stream is the first step towards video content analysis and content-based video browsing and retrieval. In this paper, we present a new algorithm mainly focused on the detection of fades using a non-common feature in previous work: entropy, a scalar representation of the amount of information conveyed by each video frame. A survey of the properties of this feature is first provided, where authors show that the pattern this series exhibits when fades occur is clear and consistent. It does not depend on the monochrome color used to fade and, in addition, it is able to deal with on-screen text that sometimes remain in the monochrome stage. A statistical model-based algorithm to detect fades is proposed. Due to the clear pattern shown by fades in the entropy series and the accurate mathematical model used, motion and illumination changes do not severely affect precision as it normally happens with algorithms dealing with the detection of gradual transitions.


False Detection Shot Boundary Detection Accurate Mathematical Model Video Content Analysis Monochrome Color 


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  1. 1.
    Boreczky, J., Rowe, L.: Comparison of video shot boundary detection techniques, SPIE, 122–128 (1996)Google Scholar
  2. 2.
    Guimaraes, S., Leite, N., Araújo, A.: A method for cut detection based on visual rhythm. In: IEEE SIBGRAPI, pp. 297–304. IEEE, Los Alamitos (2001)Google Scholar
  3. 3.
    Meng, J., Juan, Y., Chang, S.F.: Scene change detection in a mpeg comrpessed video sequence. In: IS&T SPIE Symposium for Video Technology (1995)Google Scholar
  4. 4.
    Won, J.U., Chung, Y.S., Kim, I.S., Choi, J.G., Park, K.H.: Correlation based videodissolve detection. In: IEEE International Conference on Information Technology: Research and Education, pp. 104–107 (2003)Google Scholar
  5. 5.
    Truong, B.T., Dorai, C., Venkatesh, S.: Improved fade and dissolve detection for reliable video segmentation. In: IEEE Int. Conf. Image Processing (2000)Google Scholar
  6. 6.
    Campisi, P., Neri, A., Sorgi, L.: Automatic dissolve and fade detection for video sequences. In: 14th International Conference on Digital Signal Processing (2002)Google Scholar
  7. 7.
    Han, S.H., Kweon, I.S.: Detecting cuts and dissolves through linear regression analysis. IEEE Electronic Letters 39, 1579–1581 (2003)CrossRefGoogle Scholar
  8. 8.
    Cernekova, Z., Nikou, C., Pitas, I.: Entropy metrics used for video summarization. In: Proceedings 18th conference on Computer graphics, pp. 73–82. ACM Press, New York (2002)Google Scholar
  9. 9.
    Lienhart, R., Effelsberg, W., Kuhmunch, C.: On the Detection and Recognition of Television Commercials. In: Proceedings of the International Conference on Multimedia Computing and Systems (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • José San Pedro
    • 1
  • Sergio Domínguez
    • 1
  • Nicolas Denis
    • 2
  1. 1.DISAM – ETS Ingenieros IndustrialesUniversidad Politécnica de MadridMadridSpain
  2. 2.Omnividea Multimedia 

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