Learning based objective evaluation of image segmentation algorithms

Image segmentation plays an important role in a broad range of applications and many image segmentation methods have been proposed, therefore it is necessary to be able to evaluate the performance of image segmentation algorithms objectively. In this paper we present a new fuzzy metric to evaluate the accuracy of image segmentation algorithms, based on the features of each segments using neural networks. The neural network after training can distinguish the similarity or dissimilarity of each pairs of segments and finally the segmentation algorithms accuracy have been computed by novel presented metric quantitatively. Our proposed method does not require a manually-segmented reference image for comparison therefore can be used for real-time evaluation and is sensitive to both oversegmentation and under-segmentation. Experimental results were obtained for a selection of images from Berkeley segmentation data set and demonstrated that it's a proper measure for comparing image segmentation algorithms.