Unsupervised and Supervised Image Segmentation Evaluation

Abstract

Segmentation is a fundamental step in image analysis and remains a complex problem. Many segmentation methods have been proposed in the literature but it is difficult to compare their efficiency. In order to contribute to the solution of this problem, some evaluation criteria have been proposed for the last decade to quantify the quality of a segmentation result. Supervised evaluation criteria use some a priori knowledge such as a ground truth while unsupervised ones compute some statistics in the segmentation result according to the original image. The main objective of this chapter is to first review both types of evaluation criteria from the literature. Second, a comparative study is proposed in order to identify the efficiency of these criteria for different types of images. Finally, some possible applications are presented.