Maximum Membership Scale Selection

A Classifier Combining Approach to Multi-scale Image Segmentation
  • Marco Loog
  • Yan Li
  • David M. J. Tax
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5519)


The use of multi-scale features is explored in the setting of supervised image segmentation by means of pixel classification. More specifically, we consider an interesting link between so-called scale selection and the maximum combination rule from pattern recognition. The parallel with scale selection is drawn further and a multi-scale segmentation method is introduced that relies on a per-scale classification followed by an over-scale fusion of these outcomes. A limited number of experiments is presented to provide some further understanding of the technique proposed.


Scale Space Combination Rule Quadratic Discriminant Analysis Scale Selection Maximum Membership 
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 2009

Authors and Affiliations

  • Marco Loog
    • 1
  • Yan Li
    • 1
  • David M. J. Tax
    • 1
  1. 1.Faculty of Electrical Engineering, Mathematics, and Computer ScienceDelft University of TechnologyDelftThe Netherlands

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