论文标题
使用完全知情的杜鹃搜索算法进行多级图像阈值
Multilevel Image Thresholding Using a Fully Informed Cuckoo Search Algorithm
论文作者
论文摘要
尽管在细分中有效，但常规的多级阈值方法在计算上却昂贵，因为详尽的搜索用于最佳阈值以优化目标函数。为了克服这个问题，基于人群的元启发式算法被广泛用于提高搜索能力。在本文中，我们使用基于环形拓扑的完全知情策略来改善一种流行的元疗法，称为杜鹃搜索。在这种策略中，人口中的每个人都从其社区学习，以提高人口的合作和学习效率。最佳解决方案或最佳健身值可以从初始随机阈值值中获得，其质量通过相关函数评估。已经在各种阈值上检查了实验结果。结果表明，所提出的算法比其他四种流行方法更准确和高效。
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.
