改进海鸥算法的多阈值图像分割算法北大核心CSCD

为进一步提高图像分割精度,改善传统多阈值图像分割方法计算量大、分割慢的问题,提出了改进海鸥算法(improved seagull optimization algorithm,ISOA)的多阈值图像分割方案。针对原始海鸥算法(seagull optimization algorithm,SOA)存在早熟、寻优精度不足的问题,首先,采用cubic混沌映射优化初始解,提高搜索效率;其次,引入鹰栖息优化算法(eagle perching optmizer,EPO)的缩放因子和疯狂算子进行扰动,并与麻雀搜索算法(sparow search algorithm,SSA)警戒者的位置更新相结合,优化寻优精度和收敛速度,避免陷入局部最优。利用6种基准测试函数对ISOA进行寻优性能测试。最后,将ISOA与图像分割的最优阈值选取相结合,进行基于Otsu的多阈值图像分割,并与现有分割算法进行对比。仿真结果表明,ISOA在基于Otsu的图像分割中,100%取得了最优值,且80.9%的结果优于其余算法,使图像的分割精度和质量均得到了优化。

Multi-threshold image segmentation based on improved seagull optimization algorith m

To further improve the image segmentation accuracy,improving the trad itional multi-threshold image segmentation method with large computation and slow segmentation,we proposed a multi-threshold image segmentation scheme.First,the initial solution is optimized by using the cubic chaotic mapping to improve the search efficiency.Then,scaling factors of the eagle perching optimizer (EPO) and cra zy operators are introduced for perturbation and combined with position updates of the sparrow search algorithm (SSA),to improve the optimization accuracy,convergence rate and avoiding the local optimum.The improved seagull optimization algorithm (ISOA) is tested for performance using six benchmark functions.Finally,the ISOA is combined with threshold optimal selection for multi-threshold image segmentation based on Otsu and comp ared with existing segmentation algorithms.Simulation results show that the ISOA achieves the optimal value for 100% of the Otsu-based segmentation,and 80.9% outperforms the rest,optimizing both th e segmentation accuracy and quality of the image.