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(1), 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:

03 Dec 2017 01:41

Publisher DOI:

10.1155/ASP/2006/35909

Web of Science ID:

000242074700001

BORIS DOI:

10.7892/boris.18488

URI:

https://boris.unibe.ch/id/eprint/18488 (FactScience: 634)

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