摘要
针对布谷鸟搜索算法在应用其进行图像分割时计算量大、易陷入局部极小值解、收敛速度慢的问题。文中采用一种基于改进布谷鸟搜索算法的多阈值图像分割算法。该算法以Ostu算法设计自适应度函数,将布谷鸟搜索算法和K均值算法融合,增加种群的多样性,且能自适应地确定阈值个数及其范围,并找到待分割图像的最优阈值。实验结果表明,与K均值算法和布谷鸟搜索算法相比,该算法找到的阈值质量更佳,图像分割结果更好。
The cuckoo search algorithm（ CS） is a bionic algorithm,but its application to image segmentation suffers from such drawbacks as large amount of calculation,appearing local minimum and slow convergence. In order to solve these problems,we propose a multi-threshold image segmentation algorithm based on modified cuckoo search algorithm. This algorithm employs the Otsu method as the fitness function,combines the cuckoo search algorithm with the K-means algorithm for better diversity of population,and adaptively determines the number and range of the thresholds to find the optimal thresholds of the image to be segmented. Experimental results show that the MCS algorithm outperforms the K-means and the cuckoo search（ CS） in terms of segmentation thresholds and segmentation effect.
出处
《电子科技》
2016年第5期105-107,共3页
Electronic Science and Technology
关键词
图像分割
阈值分割
K均值
image segmentation
threshold segmentation
K-means
