Video Corpus Annotation Using Active Learning

  • Stéphane Ayache
  • Georges Quénot
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4956)


Concept indexing in multimedia libraries is very useful for users searching and browsing but it is a very challenging research problem as well. Beyond the systems’ implementations issues, semantic indexing is strongly dependent upon the size and quality of the training examples. In this paper, we describe the collaborative annotation system used to annotate the High Level Features (HLF) in the development set of TRECVID 2007. This system is web-based and takes advantage of Active Learning approach. We show that Active Learning allows simultaneously getting the most useful information from the partial annotation and significantly reducing the annotation effort per participant relatively to previous collaborative annotations.


Active Learning Cold Start High Level Feature Video Shot Relevance Sampling 
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 2008

Authors and Affiliations

  • Stéphane Ayache
    • 1
  • Georges Quénot
    • 1
  1. 1.Laboratoire d’Informatique de Grenoble (LIG), 385 rue de la Bibliothèque - BP 53, 38041 Grenoble - Cedex 9France

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