HOSUR: A Novel Measure for Evaluation of Image Segmentation Quality

Abstract

Image segmentation is one of the vital tasks in image processing. Nevertheless, no universally accepted quality measure for evaluating the performance of various segmentation algorithms or even different parameterizations of the same algorithm exists. In this paper, we propose a new segmentation evaluation measure, based on the fusion of HOG and SURF features. We call it the HOSUR. HOSUR exploits the local shape and corner information to evaluate the similarity between a given segmentation and its respective ground truth. It thus belongs to the category of supervised evaluation measures. Experimental results show accuracy of up to 85%