Image segmentation is a prime operation to understand the content of images. Multilevel thresholding is applied in image segmentation because of its speed and accuracy. In this paper, a novel multilevel thresholding algorithm based on differential evolution (DE) search is introduced. One of the major drawbacks of metaheuristic algorithms is the stagnation phenomenon which leads to falling into local optimums and premature convergence. To overcome this shortcoming, the idea of Darwinian theory is incorporated with DE algorithm to increase the diversity and quality of the individuals without decreasing the convergence speed of DE algorithm. A policy of encouragement and punishment is considered to lead searching agents in the search space and reduce the computational time. The algorithm is implemented based on dividing the population into specified groups and each group tries to find a better location. Ten test images are selected to verify the ability of our algorithm using the famous energy curve method. Kapur entropy and Type 2 fuzzy entropy are employed to evaluate the capability of the introduced algorithm. Nine different metaheuristic algorithms with Darwinian modes are also implemented and compared with our method. Experimental results manifest that the proposed method is a powerful tool for multilevel thresholding and the obtained results outperform the DE algorithm and other methods.

中文翻译：

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一种用于多级图像阈值化的达尔文差分进化算法

图像分割是理解图像内容的主要操作。多级阈值处理因其速度和准确性而应用于图像分割。本文介绍了一种基于差分进化（DE）搜索的新型多级阈值算法。元启发式算法的主要缺点之一是停滞现象，导致陷入局部最优和过早收敛。为了克服这一缺点，将达尔文理论的思想与DE算法相结合，在不降低DE算法收敛速度的情况下增加个体的多样性和质量。鼓励和惩罚的策略被认为是在搜索空间中引导搜索代理并减少计算时间。该算法是基于将人口分成指定的组来实现的，每个组都试图找到一个更好的位置。选择了十张测试图像来验证我们的算法使用著名的能量曲线方法的能力。Kapur熵和Type 2模糊熵被用来评估引入算法的能力。还实现了九种不同的具有达尔文模式的元启发式算法，并与我们的方法进行了比较。实验结果表明，该方法是多级阈值化的有力工具，所获得的结果优于DE算法和其他方法。Kapur熵和Type 2模糊熵被用来评估引入算法的能力。还实现了九种不同的具有达尔文模式的元启发式算法，并与我们的方法进行了比较。实验结果表明，该方法是多级阈值化的有力工具，所获得的结果优于DE算法和其他方法。Kapur熵和Type 2模糊熵被用来评估引入算法的能力。还实现了九种不同的具有达尔文模式的元启发式算法，并与我们的方法进行了比较。实验结果表明，该方法是多级阈值化的有力工具，所获得的结果优于DE算法和其他方法。