Eisham Zubayer Kabir, Haque Md Monzurul, Rahman Md Samiur, Nishat Mirza Muntasir, Faisal Fahim, Islam Mohammad Rakibul
Department of EEE, Islamic University of Technology, Gazipur, Bangladesh.
Evol Syst (Berl). 2022 May 27:1-44. doi: 10.1007/s12530-022-09443-3.
Multilevel image thresholding and image clustering, two extensively used image processing techniques, have sparked renewed interest in recent years due to their wide range of applications. The approach of yielding multiple threshold values for each color channel to generate clustered and segmented images appears to be quite efficient and it provides significant performance, although this method is computationally heavy. To ease this complicated process, nature inspired optimization algorithms are quite handy tools. In this paper, the performance of Chimp Optimization Algorithm (ChOA) in image clustering and segmentation has been analyzed, based on multilevel thresholding for each color channel. To evaluate the performance of ChOA in this regard, several performance metrics have been used, namely, Segment evolution function, peak signal-to-noise ratio, Variation of information, Probability Rand Index, global consistency error, Feature Similarity Index and Structural Similarity Index, Blind/Referenceless Image Spatial Quality Evaluatoe, Perception based Image Quality Evaluator, Naturalness Image Quality Evaluator. This performance has been compared with eight other well known metaheuristic algorithms: Particle Swarm Optimization Algorithm, Whale Optimization Algorithm, Salp Swarm Algorithm, Harris Hawks Optimization Algorithm, Moth Flame Optimization Algorithm, Grey Wolf Optimization Algorithm, Archimedes Optimization Algorithm, African Vulture Optimization Algorithm using two popular thresholding techniques-Kapur's entropy method and Otsu's class variance method. The results demonstrate the effectiveness and competitive performance of Chimp Optimization Algorithm.
多级图像阈值处理和图像聚类是两种广泛应用的图像处理技术，近年来由于其广泛的应用范围而重新引起了人们的关注。为每个颜色通道生成多个阈值以生成聚类和分割图像的方法似乎非常有效，并且具有显著的性能，尽管这种方法计算量很大。为了简化这个复杂的过程，受自然启发的优化算法是非常方便的工具。在本文中，基于每个颜色通道的多级阈值处理，分析了黑猩猩优化算法（ChOA）在图像聚类和分割中的性能。为了评估ChOA在这方面的性能，使用了几个性能指标，即：段进化函数、峰值信噪比、信息变异、概率兰德指数、全局一致性误差、特征相似性指数和结构相似性指数、盲/无参考图像空间质量评估器、基于感知的图像质量评估器、自然度图像质量评估器。将这种性能与其他八种著名的元启发式算法进行了比较：粒子群优化算法、鲸鱼优化算法、沙丁鱼群算法、哈里斯鹰优化算法、蛾火焰优化算法、灰狼优化算法、阿基米德优化算法、非洲秃鹫优化算法，使用了两种流行的阈值处理技术——卡普尔熵方法和大津类方差方法。结果证明了黑猩猩优化算法的有效性和竞争力。
