基于粗糙熵和K-均值聚类算法的图像分割

Image Segmentation Based on Rough Entropy and K-Means Clustering Algorithm

摘要: 针对基于粗糙熵的图像分割算法不能满足复杂图像的多类目标提取的需要,本文先利用K-均值聚类算法对图像进行区域分割,再利用基于粗糙熵的方法对分割结果进行目标提取,从而达到多阈值分割的目的。通过对遥感图像进行分割处理,证明了改进后算法的有效性。

Abstract: An image segmentation algorithm is based on "rough entropy" and its computational complexity is linear.But as a method of single threshold segmentation,it can not satisfy the need for multi-class segmentation of complex image.Therefore,K-means clustering algorithm is used to segment the(image) based on region firstly,And then the algorithm based on "rough entropy" is used to extract objects from the segmentation results,so as to achieve multi-threshold segmentation.The experimental results of remote sensing image segmentation demonstrate that the improved approach is valid.