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Distance Measures for Image Segmentation Evaluation

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Author(s): Jiang Xiaoyi | Marti Cyril | Irniger Christophe | Bunke Horst

Journal: EURASIP Journal on Advances in Signal Processing
ISSN 1687-6172

Volume: 2006;
Issue: 1;
Start page: 035909;
Date: 2006;
Original page

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.

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Tangokurs Rapperswil-Jona

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