Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
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
An empirical approach to grouping and segmentation
An empirical approach to grouping and segmentation
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Toward Objective Evaluation of Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image segmentation evaluation: A survey of unsupervised methods
Computer Vision and Image Understanding
Evaluation of Segmentation Techniques Using Region Size and Boundary Information
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
Color-Based Image Salient Region Segmentation Using Novel Region Merging Strategy
IEEE Transactions on Multimedia
Ultrasound kidney segmentation with a global prior shape
Journal of Visual Communication and Image Representation
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Evaluation techniques play an important role while picking a suitable segmentation scheme out of a number of alternatives. In this paper, a novel supervised segmentation evaluation scheme is proposed that is designed by combining segment area and boundary information. Using the evaluation metric, a ranking of the popular segmentation algorithms is carried out. A comparative analysis with existing supervised metrics that are commonly used for grading segmentation schemes is performed. Experimental results indicate that the performance of the proposed measure is promising.