基于多链量子蜂群算法的模糊聚类图像分割

Fuzzy C-means clustering image segmentation method based on multichain quantum bee colony algorithm

针对传统模糊C-均值聚类算法对初始值和噪声敏感的缺点,提出了一种基于多链量子蜂群算法的模糊C-均值聚类算法。首先,将多链拓展编码方案应用到量子蜂群算法中,提出了多链量子蜂群算法;其次,利用多链量子蜂群算法来优化模糊C-均值聚类的初始聚类中心;最后,设计一种新的利用多链量子蜂群算法优化模糊C-均值聚类中心的图像分割算法。实验结果表明,所提出的基于多链量子蜂群算法的模糊C-均值聚类图像分割算法是有效的,相对于传统模糊C-均值聚类算法及基于模糊的人工蜂群算法,所提算法在分割正确率、分割速度及鲁棒性上均更有效。

In order to solve the defects of the conventional Fuzzy C-Means(FCM)clustering algorithm which is sensitive to the selection of initial values and noise data,this paper proposes an algorithm of Fuzzy C-Means clustering based on Multi-chain Quantum Bee Colony algorithm(MQBC-FCM),Firstly,it introduces the expanded multi-chains coding method to the Quantum Artificial Bee Colony(QBC)algorithm and proposes the MQBC algorithm.Then it applies the MQBC algorithm to search for the optimal initial clustering centers.In the end,it designs a new image segmentation method based on multi-chain quantum bee colony algorithm optimizing fuzzy C-means clustering centers.The experimental results show that MQBC-FCM is efficient and the proposed method performs better in segmentation accuracy,time complexity and robustness than the image segmentation algorithms of FCM and fuzzy-based ABC.