Weighted Entropy-based Measure for Image Segmentation

Abstract Image segmentation is one of the fundamental and important steps that is needed to prepare an image for further processing in many computer vision applications. Over the last few decades, many image segmentation methods have been proposed, as accurate image segmentation is vitally important for many image, video and computer vision applications. A common approach is to look at the grey level colours of the image to perform multi-level-thresholding. The ability to quantify and compare the resulting segmented images is of vital importance even though it can be a major challenge. One measure used here computes the total distances of the pixels to its centroid for each region to provide a quantifiable measure of the segmented images. We also suggest an improved Zhang's entropy measure for image segmentation based on computing the entropy of the image and segmented regions. In this paper, we will present the results from both of these approaches.