基于狮群优化的FCM图像分割算法研究

Research on Noise Image Segmentation Algorithm based on Lion Group Optimization and FCM

为了解决模糊C均值聚类(FCM)算法进行图像分割时容易陷入局部最优和随机初始化聚类中心的问题,研究人员提出了基于改进的狮群优化和模糊C均值聚类的混合图像分割算法。该算法首先利用改进的狮群算法优化模糊C均值的目标函数,增强算法全局最佳值搜索能力,使其避免陷入局部最优,同时引入聚类有效性指标,通过迭代更新搜索到合理的分割类别数实现自动确定图像分割最佳类别数,并根据最佳类别数确定最优聚类中心的选取,最终实现图像的自适应分割。实验结果表明,该方法可自适应地确定图像分割最佳类别数,能快速准确地实现图像分割。

In order to solve the problem that the Fuzzy C-means clustering (FCM) algorithm is easy to fall into the local optimal and random initial clustering center when image segmentation,a hybrid image segmentation algorithm based on improved lion group optimization and Fuzzy C-means clustering is proposed.Firstly,the improved lion group algorithm is used to optimize the objective function of Fuzzy C-means,and the global optimal value search ability of the algorithm is enhanced to avoid falling into local optimum.At the same time,the cluster validity index is introduced,and the reasonable segmentation is searched through iterative update.The number of categories realizes the automatic determination of the optimal number of image segmentation,and determines the optimal clustering center according to the optimal number of categories,and finally realizes the adaptive segmentation of the image.The experimental results show that the method can adaptively determine the optimal number of image segmentation and can realize image segmentation quickly and accurately.