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Improved Glowworm Swarm Optimization Algorithm for Multilevel Color Image Thresholding Problem

机译：改进的萤火虫群​​优化算法解决多级彩色图像阈值问题

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摘要

The thresholding process finds the proper threshold values by optimizing a criterion, which can be considered as a constrained optimization problem. The computation time of traditional thresholding techniques will increase dramatically for multilevel thresholding. To greatly overcome this problem, swarm intelligence algorithm is widely used to search optimal thresholds. In this paper, an improved glowwormswarmoptimization (IGSO) algorithmhas been presented to find the optimalmultilevel thresholds of color image based on the between-class variance and minimum cross entropy (MCE). The proposed methods are examined on standard set of color test images by using various numbers of threshold values. The results are then compared with those of basic glowworm swarm optimization, adaptive particle swarm optimization (APSO), and self-adaptive differential evolution (SaDE). The simulation results show that the proposed method can find the optimal thresholds accurately and efficiently and is an effective multilevel thresholding method for color image segmentation.
机译：阈值处理通过优化标准来找到合适的阈值，这可以被视为约束优化问题。对于多级阈值，传统阈值技术的计算时间将大大增加。为了极大地克服这个问题，群体智能算法被广泛用于搜索最佳阈值。提出了一种改进的萤火虫优化算法（IGSO），基于类间方差和最小交叉熵（MCE）找到彩色图像的最优多级阈值。通过使用各种数量的阈值，在标准的彩色测试图像集上检查提出的方法。然后将结果与基本萤火虫群优化，自适应粒子群优化（APSO）和自适应差分进化（SaDE）的结果进行比较。仿真结果表明，该方法能够准确有效地找到最优阈值，是一种有效的彩色图像分割多级阈值方法。

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  • 来源
    《Mathematical Problems in Engineering》 |2016年第9期|3196958.1-3196958.24|共24页
  • 作者

    He Lifang; Huang Songwei;

  • 作者单位

    Kunming Univ Sci & Technol, Dept Elect & Commun Engn, Kunming 650093, Peoples R China;

    Kunming Univ Sci & Technol, Dept Mineral Proc, Kunming 650093, Peoples R China;

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