Otsu method and K-means

摘要：

Otsu method is one of the most successful methods for image thresholding. This paper proves that the objective function of Otsu method is equivalent to that of Kmeans method in multilevel thresholding . They are both based on a same criterion that minimizes the within-class variance. However, Otsu method is an exhaustive algorithm of searching the global optimal threshold, while K-means is a local optimal method. Moreover, K-means does not require computing a gray-level histogram before running, but Otsu method needs to compute a gray-level histogram firstly. Therefore, K-means can be more efficiently extended to multilevel thresholding method, two-dimensional thresholding method and threedimensional method than Otsu method. This paper proved that the clustering results of K-means keep the order of the initial centroids with respect to one-dimensional data set. The experiments show that the k-means thresholding method performs well with less computing time than Otsu method does on three dimensional image thresholding.

关键词： Otsu method K-mean K-means thresholding two-dimensional thresholding three-dimensional thresholding

作者: DongjuLiu JianYu

作者单位: Department of Computer Science Beijing Jiaotong University Beijing, China

会议类型: 国际会议

会议名称: 2009 Ninth International Conference on Hybrid Intelligent Systems(第九届混合智能系统国际会议 HIS 2009)

会议地点: 沈阳

会议语种:英文

页码: 1-6

在线出版日期: 2009-08-12（万方平台首次上网日期，不代表论文的发表时间）