A novel Gini index based evaluation criterion for image segmentation

Image segmentation is the most important operation in image processing that changes the representation of an image to be useful in much computer vision applications. During the last years, several image segmentation algorithms have been developed, consequently, a great number of techniques for evaluating segmentation results have been proposed. Unfortunately, the majority of these methods is subjective and cannot be used to judge the performance of different segmentation algorithms. In this paper, we propose a novel evaluation criterion based on the Gini index and the entropy calculation. This new method permits evaluating the conjunction of the regions arrangement in the segmented image, and measuring the pixels homogeneity within each region. In experiments, benchmark images segmented by the multilevel thresholding technique based on particle swarm optimization are used to conclude the strength, the effectiveness and the rapidity of the proposed evaluation criterion.