Hierarchical Late Fusion for Concept Detection in Videos

  • Sabin Tiberius Strat
  • Alexandre Benoit
  • Hervé Bredin
  • Georges Quénot
  • Patrick Lambert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7585)


We deal with the issue of combining dozens of classifiers into a better one, for concept detection in videos. We compare three fusion approaches that share a common structure: they all start with a classifier clustering stage, continue with an intra-cluster fusion and end with an inter-cluster fusion. The main difference between them comes from the first stage. The first approach relies on a priori knowledge about the internals of each classifier (low-level descriptors and classification algorithm) to group the set of available classifiers by similarity. The second and third approaches obtain classifier similarity measures directly from their output and group them using agglomerative clustering for the second approach and community detection for the third one.


late fusion hierarchical semantic concepts video semantic indexing 


  1. 1.
    Smeaton, A.F., Over, P., Kraaij, W.: High-Level Feature Detection from Video in TRECVid: a 5-Year Retrospective of Achievements. In: Divakaran, A. (ed.) Multimedia Content Analysis, Theory and Applications, pp. 151–174. Springer, Berlin (2009)Google Scholar
  2. 2.
    Snoek, C.G.M., van de Sande, K.E.A., de Rooij, O., Huurnink, B., Gavves, E., Odijk, D., de Rijke, M., Gevers, T., Worring, M., Koelma, D.C., Smeulders, A.W.M.: The MediaMill TRECVID 2010 Semantic Video Search Engine. In: Proceedings of the 8th TRECVID Workshop, Gaithersburg, USA (2010)Google Scholar
  3. 3.
    Ng, K.B., Kantor, P.B.: Predicting the Effectiveness of Naive Data Fusion on the Basis of System Characteristics. Journal of the American Society for Information Science 51, 1177–1189 (2000)CrossRefGoogle Scholar
  4. 4.
    Newman, M.E.J.: Modularity and Community Structure in Networks. Proceedings of the National Academy of Sciences of the United States of America 103, 8577–8582 (2006)CrossRefGoogle Scholar
  5. 5.
    Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast Unfolding of Communities in Large Networks. Journal of Statistical Mechanics: Theory and Experiment 2008, P10008 (2008)Google Scholar
  6. 6.
    Ross, A.A., Nandakumar, K., Jain, A.K.: Handbook of Multibiometrics. International Series on Biometrics. Springer-Verlag New York, Inc., Secaucus (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Sabin Tiberius Strat
    • 1
    • 4
  • Alexandre Benoit
    • 1
  • Hervé Bredin
    • 2
  • Georges Quénot
    • 3
  • Patrick Lambert
    • 1
  1. 1.LISTICUniversité de SavoieAnnecyFrance
  2. 2.CNRS-LIMSIUniversité Paris-SudOrsayFrance
  3. 3.Laboratory of Informatics of GrenobleFrance
  4. 4.IPALUniversity “POLITEHNICA” of BucharestRomania

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