摘要
为了提高彩色图像的分割效果,提出一种基于改进的萤火虫优化(IGSO)算法的彩色图像多阈值分割方法,该方法以Kapur熵为目标函数。针对基本萤火虫优化(GSO)算法进化后期收敛速度慢和求解精度低的问题,采用自适应步长和添加全局信息两种策略,提出了一种改进的萤火虫优化(IGSO)算法。IGSO算法根据步长和萤火虫的移动方向对萤火虫算法收敛性的影响,在萤火虫移动过程中引入全局信息,采用随着迭代次数和搜索空间维数自适应变化步长的策略,来提高收敛性能。实验结果表明,该方法能够较好地对彩色图像进行分割,其性能优于基本的萤火虫优化(GSO)算法、改进的量子行为粒子群优化算法(CQPSO)和改进的细菌觅食算法(MBF)。
In order to improve the segmentation effect of color image,a novel multilevel color image segmentation method was presented based on an improved glowworm swarm optimization(IGSO)algorithm which uses Kapur's entropy.Aiming at the problem that the glowworm swarm optimization algorithm has low convergence speed and accuracy in the later period,an improved glowworm swarm optimization(IGSO)algorithm was presented based on adaptive step and global information.Depending on the effect of step size and the direction of movement on the convergence,IGSO algorithm improves convergence by adding global information and adaptive step with iterations and dimension of the search space during the course of movement.The experimental results show that it is a better method for multilevel color image segmentation compared with GSO algorithm,improved quantum-behaved particle swarm optimization(CQPSO)algorithm and modified bacterial foraging(MBF)algorithm.
出处
《计算机科学》
CSCD
北大核心
2017年第S1期206-211,共6页
Computer Science
基金
国家自然科学基金项目(51204077)资助
关键词
萤火虫优化算法
彩色图像分割
自适应步长
全局信息
Glowworm swarm optimization algorithm
Color image segmentation
Adaptive step
Global information
