一种快速的模糊局部C-均值聚类分割算法

针对模糊局部C-均值聚类算法计算复杂度高且对大数据样本集进行聚类时极为耗时的特点,提出了快速的模糊局部C-均值聚类分割算法。该算法将目标像素点与其邻域像素点构成的共生矩阵引入模糊局部C-均值算法,得到新的聚类隶属度和聚类中心表达式。对像素分类时,利用邻域像素隶属度进行滤波处理,进一步改善了算法的抗噪性。实验结果表明,该算法满足了图像分割有效性的需求,相较于模糊局部C-均值聚类算法,该算法具有更好的分割性能和实时性,能更好地满足实际场合图像分割的需要。

Fast Fuzzy Local Information C-means Clustering Segmentation Algorithm

A fast fuzzy local information C-means clustering segmentation algorithm was proposed because the fuzzy local information C-means clustering algorithm is time-consuming.In this algorithm,co-occurrence matrix is introduced which is constituted by the target pixel and its neighboring pixels,the new cluster membership and the cluster center expressions are obtained.To improve the noise immunity of the algorithm,neighborhood pixels membership is used for filter processing in pixel classification.The experimental results demonstrate that the proposed algorithm meets the needs of the effectiveness of image segmentation.Compared with fuzzy local information C-means clustering algorithm,the proposed algorithm has advantages of better segmentation performance and real time.