Quantitative evaluation of color image segmentation results
Pattern Recognition Letters
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
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
Geodesic Active Regions for Supervised Texture Segmentation
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Variable selection using svm based criteria
The Journal of Machine Learning Research
Genetic fusion: application to multi-components image segmentation
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 04
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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Segmentation evaluation is a very difficult task even for an expert. We propose in this article a new unsupervised evaluation criterion of an image segmentation result. The quality of a segmentation result is derived without any a priori knowledge by taking into account different evaluation criteria from the literature. We first compare six unsupervised evaluation criteria on a database composed of synthetic gray level images. Vinet's measure is used as an objective function to compare the behavior of the different criteria. We propose in this paper to fuse the best ones by a support vector machine. We illustrate the efficiency of the proposed approach through some experimental results.