自动化学报2011，Vol.37Issue(1)：11-20,10.DOI:10.3724/SP.J.1004.2011.00011
基于高斯超像素的快速Graph Cuts图像分割方法
Gaussian Super-pixel Based Fast Image Segmentation Using Graph Cuts
摘要
Abstract
This paper proposes a fast interactive image segmentation method. To achieve acceleration, the method constructs the graph cuts model using Gaussian super-pixels. The fast mean shift algorithm embedded with edge confidence is first applied to efficiently pre-segment the original image into homogenous regions with precise boundary, and these regions are described as super-pixels to construct the compact weighted graph. The feature of super-pixel is then represented by the Gaussian statistics of color information in the corresponding region, and the dissimilarity measure of Gaussians is designed in the space of information theory. Additionally, in order to learn the parameters of priori knowledge accurately and compactly, the component-wise expectation-maximization for Gaussian mixtures (CEMGM) algorithm is used to cluster the user interactions in this paper. Finally, the graph cuts algorithm is applied to the improved weighted graph model to achieve the final segmentation. Through the comparison of different color image segmentation experiments,simulation results demonstrate the superior performance of the proposed method in terms of segmentation accuracy and computation efficiency.关键词
图像分割/图切分/超像素/高斯模型/均值漂移/期望最大化算法引用本文复制引用
韩守东,赵勇,陶文兵,桑农..基于高斯超像素的快速Graph Cuts图像分割方法[J].自动化学报,2011,37(1):11-20,10.基金项目
国家高技术研究发展计划(863计划)(2009AA042107,2007AA01Z166),国家自然科学基金(61073093),国防自主创新基金资助 （863计划）
