Video Corpus Annotation Using Active Learning

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

Abstract

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.

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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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