Conceptual Image Retrieval over a Large Scale Database

  • Adrian Popescu
  • Hervé Le Borgne
  • Pierre-Alain Moëllic
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5706)


Image retrieval in large-scale databases is currently based on a textual chains matching procedure. However, this approach requires an accurate annotation of images, which is not the case on the Web. To tackle this issue, we propose a reformulation method that reduces the influence of noisy image annotations. We extract a ranked list of related concepts for terms in the query from WordNet and Wikipedia, and use them to expand the initial query. Then some visual concepts are used to re-rank the results for queries containing, explicitly or implicitly, visual cues. First evaluations on a diversified corpus of 150000 images were convincing since the proposed system was ranked 4 th and 2 nd at the WikipediaMM task of the ImageCLEF 2008 campaign [1].


image retrieval large-scale database query reformulation 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Adrian Popescu
    • 1
  • Hervé Le Borgne
    • 1
  • Pierre-Alain Moëllic
    • 1
  1. 1.CEA, LIST, Laboratoire d’ingénierie de la connaissance multimédia et multilingueFontenay-aux-RosesFrance

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