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Evaluating Image Segmentation Algorithms Using the Pareto Front (2002)
| Venue: | Proc. Seventh European Conf. Computer Vision |
| Citations: | 33 - 0 self |
BibTeX
@INPROCEEDINGS{Everingham02evaluatingimage,
author = {Mark Everingham and Mark Everingham and Henk Muller and Henk Muller and Barry Thomas and Barry Thomas},
title = {Evaluating Image Segmentation Algorithms Using the Pareto Front},
booktitle = {Proc. Seventh European Conf. Computer Vision},
year = {2002},
pages = {34--48}
}
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Abstract
Abstract. Image segmentation is the first stage of processing in many practical computer vision systems. While development of particular segmentation algorithms has attracted considerable research interest, relatively little work has been published on the subject of their evaluation. In this paper we propose the use of the Pareto front to allow evaluation and comparison of image segmentation algorithms in multi-dimensional fitness spaces, in a manner somewhat analogous to the use of receiver operating characteristic curves in binary classification problems. The principle advantage of this approach is that it avoids the need to aggregate metrics capturing multiple objectives into a single metric, and thus allows trade-offs between multiple aspects of algorithm behavior to be assessed. This is in contrast to previous approaches which have tended to use a single measure of “goodness”, or discrepancy to ground truth data. We define the Pareto front in the context of algorithm evaluation, propose several fitness measures for image segmentation, and use a genetic algorithm for multi-objective optimization to explore the set of algorithms, parameters, and corresponding points in fitness space which lie on the front. Experimental results are presented for six general-purpose image segmentation algorithms, including several which may be considered stateof-the-art.
Keyphrases
pareto front image segmentation algorithm image segmentation considerable research interest many practical computer vision system algorithm evaluation propose several fitness measure binary classification problem algorithm behavior single measure multi-objective optimization general-purpose image segmentation algorithm multiple objective multiple aspect characteristic curve genetic algorithm particular segmentation algorithm ground truth data multi-dimensional fitness space little work previous approach first stage principle advantage experimental result fitness space
