Segmentation Evaluation Using A Support Vector Machine
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. In order to identify the best criteria to fusion, we compared six unsupervised ones 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. The fusion method is based upon a support vector machine. We present in this article some experimental results of evaluation of natural gray-level images.