Population-based self-adaptive Generalised Masi Entropy for image segmentation: A novel representation

摘要

Image segmentation is an indispensable part of computer vision applications, and image thresholding is a popular one due to its simplicity and robustness. Generalised Masi entropy (GME) is an image thresholding method that exploits the additive/non-extensive information using entropic measure (r). r shows the measure of degree of extensibility and non-extensibility available in an image. From the literature, all research considered it as a fixed coefficient, while finding a proper value for r can enhance the efficacy of thresholding. This paper proposes a simple yet effective approach for adaptively finding a proper value for r without any background knowledge regarding the distribution of histogram. To this end, a new representation is proposed so that it can be used with any type of population-based metaheuristic (PBMH) algorithms. For the optimisation process, we use differential evolution (DE), as a representative. In addition, to further improve efficacy, we improve DE algorithm based on one-step k-means clustering, random-based sampling, Gaussian-based sampling, and opposition-based learning. Our extensive experiments compared to the most recent approaches on a set of benchmark images and in terms of several criteria clearly show that the proposed approach not only can find the proper value for r automatically but also it can improve the efficacy of GME-based image thresholding methods.