A Simple Feature Extraction for High Dimensional Image Representations

  • Christian Savu-Krohn
  • Peter Auer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3940)


We investigate a method to find local clusters in low dimensional subspaces of high dimensional data, e.g. in high dimensional image descriptions. Using cluster centers instead of the full set of data will speed up the performance of learning algorithms for object recognition, and might also improve performance because overfitting is avoided. Using the Graz01 database, our method outperforms a current standard method for feature extraction from high dimensional image representations.


High Dimensional Data Subspace Cluster Weak Hypothesis Dense Interval Subspace Dimension 
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 2006

Authors and Affiliations

  • Christian Savu-Krohn
    • 1
  • Peter Auer
    • 1
  1. 1.Chair of Information Technology (CIT)University of LeobenAustria

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