[1]胡春鹤,王依帆,朱书豪,等.基于鸽群优化算法的图像分割方法研究[J].郑州大学学报(工学版),2019,40(04):8.[doi:10.13705/j.issn.1671-6833.2019.04.010]
　Hu Chunhe,Wang Yifan,Zhu Shuhao,et al.Research on Image Segmentation Method Based on Pigeon Group Optimization Algorithm[J].Journal of Zhengzhou University (Engineering Science),2019,40(04):8.[doi:10.13705/j.issn.1671-6833.2019.04.010]
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基于鸽群优化算法的图像分割方法研究()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
40
期数:
2019年04期
页码:
8
栏目:
出版日期:
2019-07-10

文章信息/Info

Title:
Research on Image Segmentation Method Based on Pigeon Group Optimization Algorithm
作者:
胡春鹤; 王依帆; 朱书豪; 刘文定
北京林业大学工学院
Author(s):
Hu Chunhe; Wang Yifan; Zhu Shuhao; Liu Wending
School of Engineering, Beijing Forestry University
关键词:
鸽群优化; 图像分割; 群体智能优化; 阈值分割; 图像处理
Keywords:
Pigeon flock optimization; Image segmentation; Swarm intelligence optimization; Threshold segmentation; Image Processing
DOI:
10.13705/j.issn.1671-6833.2019.04.010
文献标志码:
A
摘要:
图像分割是一类需要在非线性参数空间中寻求最优解的有约束非线性优化问题.为提高此类优化问题的寻优精度，本文提出了一种基于鸽群优化算法的图像分割方法.首先以分割阈值为优化变量，将图像分割建模为以最大间类方差为优化目标，以像素概率分布有限为约束条件的非线性优化问题；随后，以随机的分割阈值作为迭代初值，采用鸽群优化算法（PIO）求解最优参数；最后，利用所得最优解作为最佳阈值实现图像分割.为验证本文方法的有效性，分别对具有两类不同特征的图片进行分割实验，并采用重叠度及时间效率对算法进行评估，进一步与PSO、KSW智能优化算法对比，结果表明本文算法重叠度最高，运算时间最短
Abstract:
Image segmentation is a kind of constrained nonlinear optimization problem that needs to seek the optimal solution in nonlinear parameter space. In order to improve the precision of the optimization problem, an image segmentation method based on pigeon group optimization algorithm is proposed. First, the segmentation threshold is used as the optimization variable, and the image segmentation is modeled as a nonlinear optimization problem with the optimal threshold equation as the objective function, and the inter-class variance and the w 0 and w1 ranges as the constraints. Then, Using random segmentation threshold as the initial value of iteration, the optimal parameters are solved by the pigeon group optimization algorithm(PIO). In order to verify the validity of this method, two kinds of images with different features are divided into experiments, and the algorithm is evaluated by overlapping degree and time efficiency. The results show that the algorithm has the highest degree of overlap and the shortest operation time.

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更新日期/Last Update: 2019-07-29