Performance Limit of Image Segmentation Algorithms

Image segmentation is a very important step in image analysis, and performance evaluation of segmentation algorithms plays a key role both in developing efficient algorithms and in selecting suitable methods for the given tasks. Although a number of publications have appeared on segmentation methodology and segmentation performance evaluation, little attention has been given to statistically bounding the performance of image segmentation algorithms. In this paper, a modified Cramér–Rao bound combined with the Affine bias model is employed to determine the performance limit of image segmentation algorithms. A fuzzy segmentation formulation is considered, of which hard segmentation is a special case. Experimental results are obtained where we compare the performance of several representative image segmentation algorithms with the derived bound on both synthetic and real-world image data.