Distance measures for image segmentation evaluation

Jiang, Xiaoyi; Marti, Cyril; Irniger, Christophe; Bunke, Horst (2006). Distance measures for image segmentation evaluation. EURASIP journal on applied signal processing, 2006, pp. 1-11. Akron, Ohio: Hindawi Publ. 10.1155/ASP/2006/35909

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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.

Item Type: Journal Article (Original Article)
Division/Institute: 08 Faculty of Science > Institute of Computer Science (INF)
UniBE Contributor: Bunke, Horst
ISSN: 1110-8657
Publisher: Hindawi Publ.
Language: English
Submitter: Factscience Import
Date Deposited: 04 Oct 2013 14:45
Last Modified: 08 Jun 2016 10:38
Publisher DOI: 10.1155/ASP/2006/35909
Web of Science ID: 000242074700001
BORIS DOI: 10.7892/boris.18488
URI: http://boris.unibe.ch/id/eprint/18488 (FactScience: 634)

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