Image segmentation evaluation by techniques of comparing clusterings

Authors:
Xiaoyi Jiang;Cyril Marti;Christophe Irniger;Horst Bunke
Affiliations:
Department of Computer Science, University of Münster, Münster, Germany;Institute of Informatics and Applied Mathematics, University of Bern, Bern, Switzerland;Institute of Informatics and Applied Mathematics, University of Bern, Bern, Switzerland;Institute of Informatics and Applied Mathematics, University of Bern, Bern, Switzerland
Venue:
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Year:
2005

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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.