A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
A critical investigation of recall and precision as measures of retrieval system performance
ACM Transactions on Information Systems (TOIS)
A Unified Approach to the Change of Resolution: Space and Gray-Level
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
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
Towards Perceptually Driven Segmentation Evaluation Metrics
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 4 - Volume 04
Toward a generic evaluation of image segmentation
IEEE Transactions on Image Processing
Balancing the Role of Priors in Multi-Observer Segmentation Evaluation
Journal of Signal Processing Systems
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
A subjective method for image segmentation evaluation
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Segmentation of objects in a detection window by Nonparametric Inhomogeneous CRFs
Computer Vision and Image Understanding
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In spite of significant advances in image segmentation techniques, evaluation of these methods thus far has been largely subjective. Typically, the effectiveness of a new algorithm is demonstrated only by the presentation of a few segmented images that are evaluated by some method, or it is otherwise left to subjective evaluation by the reader. We propose a new approach for evaluation of segmentation that takes into account not only the accuracy of the boundary localization of the created segments but also the under-segmentation and over-segmentation effects, regardless to the number of regions in each partition. In addition, it takes into account the way humans perceive visual information. This new metric can be applied both to automatically provide a ranking among different segmentation algorithms and to find an optimal set of input parameters of a given algorithm.