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Segmentation evaluation by fusion with a genetic algorithm | IEEE Conference Publication | IEEE Xplore

Segmentation evaluation by fusion with a genetic algorithm


Abstract:

The goal of this work is to be able to quantify the quality of a segmentation result without any a priori knowledge. We propose in this article to fusion different unsupe...Show More

Abstract:

The goal of this work is to be able to quantify the quality of a segmentation result without any a priori knowledge. We propose in this article to fusion different unsupervised evaluation criteria. In order to identify the best ones to fusion, we compared 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. A new criterion is derived by linearly combining the best ones. The linear coefficients are determined by maximizing the correlation factor with the Vinet's measure by a genetic algorithm. We present in this article some experimental results of evaluation of natural gray-level images.
Date of Conference: 04-08 September 2005
Date Added to IEEE Xplore: 06 April 2015
Print ISBN:978-160-4238-21-1
Conference Location: Antalya, Turkey

References

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