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A New Supervised Evaluation Criterion for Region Based Segmentation Methods

  • Adel Hafiane
  • Sébastien Chabrier
  • Christophe Rosenberger
  • Hélène Laurent
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4678)

Abstract

We present in this article a new supervised evaluation criterion that enables the quantification of the quality of region segmentation algorithms. This criterion is compared with seven well-known criteria available in this context. To that end, we test the different methods on natural images by using a subjective evaluation involving different experts from the French community in image processing. Experimental results show the benefit of this new criterion.

Keywords

Ground Truth Image Segmentation Segmentation Result Average Standard Deviation Supervise Evaluation 
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 2007

Authors and Affiliations

  • Adel Hafiane
    • 1
  • Sébastien Chabrier
    • 1
  • Christophe Rosenberger
    • 1
  • Hélène Laurent
    • 1
  1. 1.Laboratoire Vision et Robotique - UPRES EA 2078, ENSI de Bourges - Université d’Orléans, 88 boulevard Lahitolle, 18020 Bourges CedexFrance

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