基于混沌粒子群和模糊聚类的图像分割算法研究

针对模糊C-均值聚类算法受初始聚类中心和隶属度矩阵的影响,易陷入局部最优解,以至于得不到最佳聚类结果等问题,提出了一种新的基于混沌粒子群的模糊C-均值聚类的图像分割算法。该算法采用逻辑自映射函数初始化均匀分布的粒子群,当算法陷入早熟收敛时进行混沌优化,以改善因粒子停滞而收敛到局部最优解的能力。实验结果表明,该算法具有更快的分割速度和更高的分割精度。

Research of Image Segmentation Algorithm Based on Chaos Particle Swarm and Fuzzy Clustering

Fuzzy C-means clustering algorithm is sensitive to initial clustering center and membership matrix and likely converges into the local minimum, so it can not get the best clustering results. A new image segmentation algorithm based on chaos particle swarm and FCM clustering is proposed. The uniform particles are produced by logical self-map function.When it gets into the premature convergence,the algorithm can start the chaos optimization to improve the performance of convergence into the local minimum because of standstill. The experimental results show that the new algorithm has faster convergence and higher accuracy of segmentation.