The Adaptive Subspace Map for Image Description and Image Database Retrieval

  • Dick de Ridder
  • Olaf Lemmers
  • Robert P. W. Duin
  • Josef Kittler
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1876)


In this paper, a mixture-of-subspaces model is proposed to describe images. Images or image patches, when translated, rotated or scaled, lie in low-dimensional subspaces of the high-dimensional space spanned by the grey values. These manifolds can locally be approximated by a linear subspace. The adaptive subspace map is a method to learn such a mixture-of-subspaces from the data. Due to its general nature, various clustering and subspace-finding algorithms can be used. If the adaptive subspace map is trained on data extracted from images, a description of the image content is obtained, which can then be used for various classification and clustering problems. Here, the method is applied to an image database retrieval problem and an object image classification problem, and is shown to give promising results.


Training Image Mahalanobis Distance Object Image Query Image Independent Component Analysis 
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 2000

Authors and Affiliations

  • Dick de Ridder
    • 1
    • 2
  • Olaf Lemmers
    • 1
  • Robert P. W. Duin
    • 1
  • Josef Kittler
    • 2
  1. 1.Pattern Recognition Group Dept. of Applied Physics, Faculty of Applied SciencesDelft University of TechnologyDelftThe Netherlands
  2. 2.Centre for Vision, Speech and Signal Processing, Dept. of Electronic & Electrical Engineering, School of EEITMUniversity of SurreyGuildfordSurreyUK

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