Unsupervised performance evaluation of image segmentation
EURASIP Journal on Applied Signal Processing
Image segmentation evaluation: A survey of unsupervised methods
Computer Vision and Image Understanding
International Journal of Bio-Inspired Computation
Goal evaluation of segmentation algorithms for traffic sign recognition
IEEE Transactions on Intelligent Transportation Systems
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
A stochastic gravitational approach to feature based color image segmentation
Engineering Applications of Artificial Intelligence
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We present in this article a study of some unsupervised evaluation criteria of an image segmentation result. The goal of this work is to be able to automatically choose the parameters of a segmentation method best fitted for an image or to fusion different segmentation results. We compared six unsupervised evaluation criteria on a database composed of 100 synthetic gray-level images segmented by four methods. Vinet's measure is used as an objective function to compare the behavior of the different criteria. We finally apply these criteria to evaluate segmentation results of multi-components images. We present in this article some experimental results of evaluation of gray-level and multi-components natural images.