参数自适应的可变类FLICM灰度图像分割算法

为解决传统FLICM算法需人为给定图像聚类数的问题,基于该算法通过聚类中心描述聚类的特点,设计以聚类中心为操作对象的分裂合并操作,以实现可变类图像分割.在此基础上定义分裂合并操作的接受率,不但能够有效避免算法陷入局部极值,促进其快速收敛,同时有利于参数阈值的自适应.分别利用所提出算法和传统ISODATA算法分割模拟图像和灰度纹理图像,对其结果的定性定量分析验证了所提出算法的有效性和普适性.

Self-adaptive FLICM algorithm for gray image segmentation with unknown number of clusters

To determine the number of clusters in the traditional FLICM algorithm, this paper designs splitting-merging operations based on the characteristics of the FLICM algorithm which uses the mean of pixels to describe the cluster. Then, an acceptance probability is designed to promote the convergence of the algorithm, which can prevent it from being trapped into local extremes and realize the self-adaptiveness of the thresholds, simultaneously. Qualitative and quantitative analysis on the experimental results from a series of synthetic and gray texture image segmentation demonstrates the adaptability and accuracy of the proposed approach compared with the traditional iterative self organizing data analysis techniques algorithm(ISODATA).