2006
DOI: 10.1155/asp/2006/35909
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Distance Measures for Image Segmentation Evaluation

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 p… Show more

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Cited by 77 publications
(67 citation statements)
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References 22 publications
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“…Instead of using a single measure for quantifying the segmentation quality, some authors support the idea of taking a collection of similar measures for defining an overall performance measure [48]. In a global sense, our proposal has reached the best performance in terms of the quantitative measures for the segmentation goodness, obtaining the best results in three out of four of them.…”
Section: Discussionmentioning
confidence: 98%
“…Instead of using a single measure for quantifying the segmentation quality, some authors support the idea of taking a collection of similar measures for defining an overall performance measure [48]. In a global sense, our proposal has reached the best performance in terms of the quantitative measures for the segmentation goodness, obtaining the best results in three out of four of them.…”
Section: Discussionmentioning
confidence: 98%
“…This idea was presented in [4] as symmetric partition-distance, in [14] as bipartite-graphmatching (BGM) distance, and in the context of clustering comparison, in [22] as classification error distance. It is shown in [4] that it is equivalent to the minimum number of pixels that must not be taken into account for the two partitions to be identical.…”
Section: Pixel-set Clusteringmentioning
confidence: 99%
“…1 and 2 (and Figs. 4 and 5) were used as reference ground truths (Jiang et al 2006) for those two patterns (Fernandez-Garcıa et al 2008). Then an EDA was performed for each pattern to determine their typical EGD from RGB cube diagonal.…”
Section: B Cloud and Sky Pattern Characterization With Exploratory Dmentioning
confidence: 99%