摘要
图像分割是图像分析及图像理解的关键步骤。与其他图像分割算法相比,均值漂移(Mean Shift)算法具有原理简单、无需先验知识、可以处理灰度图像及复杂的自然彩色图像等优点。但该算法需要对图像中每个像素点进行迭代计算,因此分割所需要的时间较长。本文提出了一种快速Mean Shift图像分割算法(Fast mean shift,FMS),将少量像素点作为初始点进行迭代计算,而出现在高维球区域内的其他像素点根据其到已有类中心的距离进行归类,从而减少Mean Shift算法的迭代次数,缩短分割时间。实验结果表明,本文提出的快速Mean Shift图像分割算法可以获得良好的分割结果且具有较高的分割效率。
Image segmentation is a key step in image analysis and image understanding.Compared with other image segmentation algorithms,mean shift algorithm has some advantages such as simple principle,dispensing with a priori knowledge,capability of dealing with gray images and complex natural color images,etc.However,the algorithm requires iterative calculations for each pixel in the image,and segmentation computational cost is high for practical tasks.Therefore,a fast mean shift（FMS）method for image segmentation is proposed,in which a small amount of pixels are selected as an initial point for iterative calculation,and other pixels are mergered to the existing classes according to the distance between the pixel and the class centers.As a result,the proposed FMS method reduces the iteration numbers of mean shift algorithm,and boosts the segmentation efficiency.Experimental results show that the proposed FMS method can obtain good segmentation results and higher segmentation efficiency.
出处
《数据采集与处理》
CSCD
北大核心
2015年第1期192-201,共10页
Journal of Data Acquisition and Processing
基金
国家自然科学基金(61273291)资助项目
山西省回国留学人员科研(2012-008)资助项目
山西省科技攻关计划(20120321027-01)资助项目
关键词
图像分割
均值飘移
聚类
归并
image segmentation
mean shift
clustering
merging
