Open Access

Unsupervised Performance Evaluation of Image Segmentation

  • Sebastien Chabrier1Email author,
  • Bruno Emile1,
  • Christophe Rosenberger1 and
  • Helene Laurent1
EURASIP Journal on Advances in Signal Processing20062006:096306

https://doi.org/10.1155/ASP/2006/96306

Received: 1 March 2005

Accepted: 21 January 2006

Published: 27 March 2006

Abstract

We present in this paper a study of unsupervised evaluation criteria that enable the quantification of the quality of an image segmentation result. These evaluation criteria compute some statistics for each region or class in a segmentation result. Such an evaluation criterion can be useful for different applications: the comparison of segmentation results, the automatic choice of the best fitted parameters of a segmentation method for a given image, or the definition of new segmentation methods by optimization. We first present the state of art of unsupervised evaluation, and then, we compare six unsupervised evaluation criteria. For this comparative study, we use a database composed of 8400 synthetic gray-level images segmented in four different ways. Vinet's measure (correct classification rate) is used as an objective criterion to compare the behavior of the different criteria. Finally, we present the experimental results on the segmentation evaluation of a few gray-level natural images.

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Authors’ Affiliations

(1)
Laboratoire Vision et Robotique, UPRES EA 2078,ENSI de Bourges, Universit$#233; d' Orl$#233;ans

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Copyright

© Chabrier et al. 2006