[1]李鑫鑫,刘群锋.基于改进人工蜂群算法的多阈值图像分割[J].计算机技术与发展,2023,33(05):75-80.[doi:10. 3969 / j. issn. 1673-629X. 2023. 05. 012]
　LI Xin-xin,LIU Qun-feng.Multi-threshold Image Segmentation Based on Improved Artificial Bee Colony Algorithm[J].,2023,33(05):75-80.[doi:10. 3969 / j. issn. 1673-629X. 2023. 05. 012]

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基于改进人工蜂群算法的多阈值图像分割()

分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:

33

期数:

2023年05期

页码:

75-80

栏目:

媒体计算

出版日期:

2023-05-10

文章信息/Info

Title:

Multi-threshold Image Segmentation Based on Improved Artificial Bee Colony Algorithm

文章编号:

1673-629X(2023)05-0075-06

作者:

李鑫鑫; 刘群锋

东莞理工学院 计算机学院,广东 东莞 523808

Author(s):

LI Xin-xin; LIU Qun-feng

School of Computing,Dongguan University of Technology,Dongguan 523808,China

关键词:

多阈值分割; 人工蜂群算法; DIRECT 算法; 最大类间方差; 最小交叉熵

Keywords:

multi - threshold segmentation;  artificial bee colony algorithm;  DIRECT algorithm;  maximum between - class variance; minimum cross entropy

分类号:

TP391. 4

DOI:

10. 3969 / j. issn. 1673-629X. 2023. 05. 012

摘要:

图像分割在模式识别以及机器视觉方面起着至关重要的作用,是图像分析和识别的首要任务。 但若分割后图像质量损失严重,就会导致图像后续分析的误差增加。 为了能够弥补这一缺陷,在分析了 DIRECT 算法和人工蜂群算法的特性后,利用 DIRECT 算法全局收敛并可以快速定位到最优值所在区域的特点来改善人工蜂群算法的过早收敛以及局部搜索能力差的缺点,提出了一种基于改进的人工蜂群算法的多阈值图像分割技术。 首先,DIRECT 算法为人工蜂群算法提供一种良好的初始种群,种群在演化数代后得到的当前最优解加入到 DIRECT 算法分割区域中,再进行初始种群的筛选,重复这个过程进而获得最佳阈值并对图像进行分割。 为了验证该算法的优劣性,使用峰值信噪比、结构相似性以及特征相似性作为图像质量评价指标并与前人得到的结果进行比较。 实验数据表明,提出的阈值分割方法优于前人的阈值分割方法。

Abstract:

Image segmentation plays an important role in pattern recognition and computer vision,which is the primary task for imageanalysis and recognition. However,it will increase errors if?

the quality of the segmented image is seriously lost. To solve this drawback,the multi-threshold image segmentation based on improving artificial bee colony algorithm is proposed after?

analyzing the characteristic ofDIRECT and artificial bee colony algorithm. Artificial bee colony algorithm has weakness of premature convergence and poor localsearch capability,while DIRECT algorithm can improve its deficiencies. The DIRECT algorithm can find a good initial population forartificial bee colony algorithm,and then the current optimum solution can be obtained and joined into the DIRECT’s partitions afterseveral generations of evolution of the population. Keep repeating this process until the stop condition is satisfied. To verify the validity

of the proposed algorithm,we adopt the peak signal-to-noise ratio,structural similarity and feature similarity as image quality evaluationindexes and compare with the results obtained by predecessors. The numerical results show that the proposed algorithm proposed is betterthan the former algorithms.

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