基于改进布谷鸟优化的模糊聚类图像分割

模糊C均值聚类算法(FCM)是一种应用非常广泛的聚类算法,但是它受初始聚类中心影响较大,容易陷入局部最优。 在标准布谷鸟算法(CS)的基础上 提出改进布谷鸟优化算法(ICS),将发现概率P由固定值转变成随迭代次数逐渐减小的变量,这样不仅可以提高搜索种群的质量,而且保证了算法的收敛。因此,可以将改进布谷鸟优化算法用于FCM算法聚类中心生成的过程(ICS_FCM),从而有效地避免FCM陷入局部最优。改进的算法具有良好的聚类效果和运行速度。实现基于改进布谷鸟优化的FCM图像分割,并与基于模拟退火的FCM算法(SA_FCM)进行对比。由实验结果可知,该算法(ICS_FCM)不仅取得了较好的分割效果,效率上也有明显的提高。

Fuzzy Clustering Image Segmentation Algorithm Based on Improved Cuckoo Search

Fuzzy C-means clustering algorithm(FCM) is a widely used clustering algorithm,however,it is influenced by the initial cluster centers,and is easy to fall into local optima.In this article,we proposed an improved cuckoo search(ICS) based on the standard cuckoo algorithm(CS),which changes the detection probability P with a constant value into a variable number of iterations decreases.This will not only improve the quality of the population,but also ensure the convergence of the algorithm.Therefore,we can use the improved cuckoo search algorithm to generate the FCM clustering centers and avoid FCM falling into local optima effectively.The proposed algorithm has better clustering effect and faster running speed.In this article,ICS_FCM was used in fuzzy clustering image segmentation,and compared with SA_FCM.The experimental results show that ICS_FCM can not only achieve better segmentation results,but also improved efficiency significantly.