一种基于改进GS方法的图像分割方法

An Image Segmentation Method Based on Improved GS Method

聚类分析作为多元统计分析的一个分支,用于图像分割己有相当一段历史。文章基于间隙统计(Gap Statistic,GS)方法从估计最佳聚类数的角度对图像存在的最佳分割进行研究。为去除GS方法中由于随机产生参考数集以及采用样本估计带来的误差,并能给出一个具体的图像分割过程,文章对GS方法进行了改进,通过修正间隙(Gap)统计量,计算出一维情况下Gap统计量的具体函数表达式,并在此基础上提出一种图像分割算法:最佳自适应k-阈值分割算法。

Clustering analysis has been employed in image segmentation for a long time. It is aimed at the best output of image segmentation in this article. In order to remove the error caused by random reference number set and sample estimation in GS method, and to get an operational procedure in image segmentation, the GS method has been modified, and the concretely expression of the Gap statistic is computed in the condition of 1-D by amending the Gap statistic. Furthermore, an algorithm is put forward to segment image by choosing threshold： the best adaptive k-threshold segmentation.