Content-Based Video Description for Automatic Video Genre Categorization

  • Bogdan Ionescu
  • Klaus Seyerlehner
  • Christoph Rasche
  • Constantin Vertan
  • Patrick Lambert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7131)


In this paper, we propose an audio-visual approach to video genre categorization. Audio information is extracted at block-level, which has the advantage of capturing local temporal information. At temporal structural level, we asses action contents with respect to human perception. Further, color perception is quantified with statistics of color distribution, elementary hues, color properties and relationship of color. The last category of descriptors determines statistics of contour geometry. An extensive evaluation of this multi-modal approach based on on more than 91 hours of video footage is presented. We obtain average precision and recall ratios within [87% − 100%] and [77% − 100%], respectively, while average correct classification is up to 97%. Additionally, movies displayed according to feature-based coordinates in a virtual 3D browsing environment tend to regroup with respect to genre, which has potential application with real content-based browsing systems.


video genre classification block-level audio features action segmentation color perception contour geometry video indexing 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Bogdan Ionescu
    • 1
    • 3
  • Klaus Seyerlehner
    • 2
  • Christoph Rasche
    • 1
  • Constantin Vertan
    • 1
  • Patrick Lambert
    • 3
  1. 1.LAPIUniversity “Politehnica” of BucharestBucharestRomania
  2. 2.DCPJohannes Kepler UniversityLinzAustria
  3. 3.LISTICUniversity of SavoieAnnecy-le-Vieux CedexFrance

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