结合EFD与NCut的彩色图像分割方法  

Color Image Segmentation Approach by Combining EFD and NCut

作　　者：徐丽燕[1] 张洁玉[1] 孙权森[1] 夏德深[1] 

机构地区：[1]南京理工大学计算机科学与技术学院,南京210094

出　　处：《模式识别与人工智能》2010年第5期671-677,共7页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金(No.60773172);江苏省自然科学基金(No.BK2008411);教育部博士学科点基金(No.200802880017)资助项目

摘　　要：为了克服边缘流引导的各向异性扩散(EFD)方法过分割和归一化分割(NCut)方法计算复杂度高的缺点,提出结合EFD和NCut的彩色图像分割方法.首先利用EFD对图像进行预分割,然后将分割区域作为节点构建带权无向图G,用NCut对图进行全局最优化分类,并进行相应后处理,得到最终结果.由于图G是基于过分割区域而非像素点的,所以算法效率得到较大提高.另外,EFD方法可有效利用图像的局部信息,NCut方法则考虑到图像的全局特征,因此文中方法综合两者的优点.实验结果表明,文中方法能够取得较好的分割效果.To overcome the over segmentation phenomenon of edgeflow-driven anisotropic diffusion （EFD） and the high computational complexity of normalized cut （NCut） , a color image segmentation algorithm based on EFD and NCut is presented. EFD is applied to the image to get a preliminary result. Then, the segmented regions are taken applied to perform globally as nodes to construct a optimized clustering. weighted undirected graph G, and the NCut is Segmentation results are achieved after proper post-process. The graph structure is based on segmented regions instead of image pixels, and thus the proposed algorithm requires lower computational complexity. In addition, EFD focuses on local detail while NCut captures global property, so this algorithm combines both advantages. Experimental results show that this algorithm can get appropriate segmentation results.

关 键 词：彩色图像分割 边缘流引导的各向异性扩散(EFD) 归一化分割(NCut) 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]