结合EFD与NCut的彩色图像分割方法

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。

Color Image Segmentation Approach by Combining EFD and NCut

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 as nodes to construct a weighted undirected graph G, and the NCut is applied to perform globally optimized clustering. 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.