Relevance Ranking of Video Data Using Hidden Markov Model Distances and Polygon Simplification

  • Daniel DeMenthon
  • Longin Jan Latecki
  • Azriel Rosenfeld
  • Marc Vuilleumier Stückelberg
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1929)


A video can be mapped into a multidimensional signal in a non-Euclidean space, in a way that translates the more predictable passages of the video into linear sections of the signal. These linear sections can befiltered out by techniques similar to those used for simplifying planar curves. Different degrees of simplification can be selected. We have refined such a technique so that it can make use of probabilistic distances between statistical image models of the video frames. These models are obtained by applying hidden Markov model techniques to random walks across the images. Using our techniques, a viewer can browse a video at the level of summarization that suits his patience level. Applications include the creation of a smart fast-forward function for digital VCRs, and the automatic creation of short summaries that can be used as previews before videos are downloaded from the web.


Hide Markov Model Video Data Video Summarization Video Player Image Stream 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Daniel DeMenthon
    • 1
  • Longin Jan Latecki
    • 2
  • Azriel Rosenfeld
    • 1
  • Marc Vuilleumier Stückelberg
    • 3
  1. 1.Center for Automation ResearchUniversity of MarylandUSA
  2. 2.Department of Applied MathematicsUniversity of HamburgHamburgGermany
  3. 3.Computer Science DepartmentCUI, University of GenevaGeneva 4

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