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Distance Measures for Image Segmentation Evaluation

  • Xiaoyi Jiang
  • Cyril Marti
  • Christophe Irniger
  • Horst Bunke
Open Access
Research Article
Part of the following topical collections:
  1. Performance Evaluation in Image Processing

Abstract

The task considered in this paper is performance evaluation of region segmentation algorithms in the ground-truth-based paradigm. Given a machine segmentation and a ground-truth segmentation, 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 image processing. 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

Image Processing Information Technology Machine Learning Performance Evaluation Real Data 
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

© Jiang et al. 2006

Authors and Affiliations

  • Xiaoyi Jiang
    • 1
  • Cyril Marti
    • 2
  • Christophe Irniger
    • 2
  • Horst Bunke
    • 2
  1. 1.Computer Vision and Pattern Recognition Group, Department of Computer ScienceUniversity of MünsterMünsterGermany
  2. 2.Institute of Computer Science and Applied MathematicsUniversity of BernBernSwitzerland

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