2005
DOI: 10.1007/11556121_35
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Design of Statistical Measures for the Assessment of Image Segmentation Schemes

Abstract: Image segmentation is discussed for years in numerous papers, but assessing its quality is mainly dealt with in recent works. Quality assessment is a primary concern for anyone working towards better segmentation tools. It both helps to objectively improve segmentation techniques and to compare performances with respect to other similar algorithms. In this paper we use a statistical framework to propose statistical measures capable to describe the performances of a segmentation scheme. All the measures rely on… Show more

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Cited by 10 publications
(8 citation statements)
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“…discrepancy methods evaluated segmented images based on the number of misclassified pixels versus the reference image, with penalties weighted proportional to the distance to the closest correctly classified pixel for that region [10][11][12][13][14]. Another group of discrepancy methods are based on the differences in the feature values measured from the segmented images and the reference image [15][16][17][18][19][20]. These methods have been extended to accommodate the problem when the number of objects differs between the segmented and reference images [21][22][23][24][25].…”
Section: Supervised Evaluation Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…discrepancy methods evaluated segmented images based on the number of misclassified pixels versus the reference image, with penalties weighted proportional to the distance to the closest correctly classified pixel for that region [10][11][12][13][14]. Another group of discrepancy methods are based on the differences in the feature values measured from the segmented images and the reference image [15][16][17][18][19][20]. These methods have been extended to accommodate the problem when the number of objects differs between the segmented and reference images [21][22][23][24][25].…”
Section: Supervised Evaluation Methodsmentioning
confidence: 99%
“…19), to describe the inter-region disparity. C is the sum of the per-region contrast measures, weighted by a function approximating the human contrast sensitivity curve.…”
Section: Inter-region Disparity Metricsmentioning
confidence: 99%
“…Еще одна мера оценки качества сегментации, основанная на подсчете неправильно классифицирован-ных пикселей и использующая байесовский подход, была предложена в [11]. В этой работе для случая одного сегмента вычисляются оценки вероятности того, что случайно выбранный пиксель на отсегмен-тированном изображении принадлежит сегменту и, соответственно, фону.…”
Section: критерии оценки качества результатов работы программunclassified
“…[11][12][13][14][15][25][26][27][28][29][30][31][32] Since the construction of the ground-truth segmentation for many real images is labor-intensive and sometimes not well or uniquely defined, most prior image segmentation methods are only tested on ͑1͒ some special classes of images used in special applications where the ground-truth segmentations are uniquely defined, ͑2͒ synthetic images where ground-truth segmentation is also well defined, and/or ͑3͒ a small set of real images.…”
Section: Related Work and Our Contributionmentioning
confidence: 99%