基于完全信息布谷鸟搜索算法的多级图像保持
Multilevel Image Thresholding Using a Fully Informed Cuckoo Search Algorithm
摘要：
虽然有效的分割，传统的多级阈值的方法是计算昂贵的穷举搜索用于最佳阈值，以优化目标函数。为了克服这个问题，基于种群的元启发式算法被广泛用于提高搜索能力。在本文中，我们改进了一个流行的元启发式称为布谷鸟搜索使用环拓扑结构的完全知情的战略。在该策略中，种群中的每个个体都从其邻域中学习，以提高种群的合作性和学习效率。该方法从初始随机阈值出发，得到最优解或最佳适应值，并通过相关函数评价其优劣。实验结果已被检查的各种数量的阈值。实验结果表明，该算法比其他四种常用的方法具有更高的精度和效率。
英文摘要：
Though effective in the segmentation, conventional multilevel thresholding methods are computationally expensive as exhaustive search are used for optimal thresholds to optimize the objective functions. To overcome this problem, population-based metaheuristic algorithms are widely used to improve the searching capacity. In this paper, we improve a popular metaheuristic called cuckoo search using a ring topology based fully informed strategy. In this strategy, each individual in the population learns from its neighborhoods to improve the cooperation of the population and the learning efficiency. Best solution or best fitness value can be obtained from the initial random threshold values, whose quality is evaluated by the correlation function. Experimental results have been examined on various numbers of thresholds. The results demonstrate that the proposed algorithm is more accurate and efficient than other four popular methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
