Elements of information theory
Elements of information theory
An Experimental Comparison of Range Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
An Adaptive Contour Closure Algorithm and Its Experimental Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Yet Another Survey on Image Segmentation: Region and Boundary Information Integration
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Quantitative methods of evaluating image segmentation
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 3)-Volume 3 - Volume 3
Supervised Evaluation Methodology for Curvilinear Structure Detection Algorithms
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Performance criteria for graph clustering and Markov cluster experiments
Performance criteria for graph clustering and Markov cluster experiments
Optimal Range Segmentation Parameters through Genetic Algorithms
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Some Further Results of Experimental Comparison of Range Image Segmentation Algorithms
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Comparing Curved-Surface Range Image Segmenters
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Image segmentation evaluation by techniques of comparing clusterings
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Automated performance evaluation of range image segmentation algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A similarity metric for edge images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Towards Fully Automatic Image Segmentation Evaluation
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Morphological segmentation on learned boundaries
Image and Vision Computing
Partition-distance methods for assessing spatial segmentations of images and videos
Computer Vision and Image Understanding
Computers in Biology and Medicine
A comparative evaluation of interactive segmentation algorithms
Pattern Recognition
A new supervised evaluation criterion for region based segmentation methods
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
An open approach towards the benchmarking of table structure recognition systems
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
Learning a nonlinear distance metric for supervised region-merging image segmentation
Computer Vision and Image Understanding
Toward automated evaluation of interactive segmentation
Computer Vision and Image Understanding
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Segmentation of objects in a detection window by Nonparametric Inhomogeneous CRFs
Computer Vision and Image Understanding
Filling the gap in quality assessment of video object tracking
Image and Vision Computing
A clustering-based ensemble technique for shape decomposition
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
A region-based method for sketch map segmentation
GREC'11 Proceedings of the 9th international conference on Graphics Recognition: new trends and challenges
A new evaluation measure for color image segmentation based on genetic programming approach
Image and Vision Computing
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The task considered in this paper is performance evaluation of region segmentation lgorithms 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.