Image Segmentation Evaluation by Techniques of Comparing Clusterings

  • Xiaoyi Jiang
  • Cyril Marti
  • Christophe Irniger
  • Horst Bunke
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3617)

Abstract

The task considered in this paper is performance evaluation of region segmentation algorithms in the ground truth (GT) based paradigm. Given a machine segmentation and a GT reference, performance measures are needed. We propose to consider the image segmentation problem as one of data clustering and, as a consequence, to use measures for comparing clusterings developed in statistics and machine learning. By doing so, we obtain a variety of performance measures which have not been used before in computer vision. In particular, some of these measures have the highly desired property of being a metric. Experimental results are reported on both synthetic and real data to validate the measures and compare them with others.

Keywords

Ground Truth Image Segmentation Segmentation Algorithm Range Image Region Segmentation 
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 2005

Authors and Affiliations

  • Xiaoyi Jiang
    • 1
  • Cyril Marti
    • 2
  • Christophe Irniger
    • 2
  • Horst Bunke
    • 2
  1. 1.Department of Computer ScienceUniversity of MünsterMünsterGermany
  2. 2.Institute of Informatics and Applied MathematicsUniversity of BernBernSwitzerland

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