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Content-Based Image Retrieval by Indexing Random Subwindows with Randomized Trees

  • Raphaël Marée
  • Pierre Geurts
  • Louis Wehenkel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4844)

Abstract

We propose a new method for content-based image retrieval which exploits the similarity measure and indexing structure of totally randomized tree ensembles induced from a set of subwindows randomly extracted from a sample of images. We also present the possibility of updating the model as new images come in, and the capability of comparing new images using a model previously constructed from a different set of images. The approach is quantitatively evaluated on various types of images with state-of-the-art results despite its conceptual simplicity and computational efficiency.

Keywords

Recognition Rate Image Retrieval Training Image Query Image Scalable Invariant Feature Transform 
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 2007

Authors and Affiliations

  • Raphaël Marée
    • 1
  • Pierre Geurts
    • 2
  • Louis Wehenkel
    • 2
  1. 1.GIGA Bioinformatics Platform, University of LiègeBelgium
  2. 2.Systems and Modeling Unit, Montefiore Institute, University of LiègeBelgium

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