文献类型：期刊文献

英文题名：GrabCut image segmentation algorithm based on structure tensor

作者：Yong, Zhang[1]; Jiazheng, Yuan[1,2]; Hongzhe, Liu[1]; Qing, Li[1]

第一作者：Yong, Zhang

通讯作者：Jiazheng, Yuan

机构：[1] Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing, 100101, China; [2] Beijing High-Tech Innovation Centre of Imaging Technology, Capital Normal University, Beijing, 100048, China

第一机构：北京联合大学北京市信息服务工程重点实验室

年份：2017

卷号：24

期号：2

起止页码：38-47

外文期刊名：Journal of China Universities of Posts and Telecommunications

收录：EI(收录号：20172903960401)

语种：英文

外文关键词：Efficiency - Gaussian distribution - Geometry - Iterative methods - Tensors - Textures

摘要：This paper attempts to present an interactive color natural images segmentation method. This method extracts the feature of images by using the nonlinear compact structure tensor (NCST) and then uses GrabCut method to obtain the segmentation. This method not only realizes the non-parametric fusion of texture information and color information, but also improves the efficiency of the calculation. Then, the improved GrabCut algorithm is used to evaluate the foreground target segmentation. In order to calculate the simplicity and efficiency, this paper also extends the Gaussian mixture model (GMM) constructed base on the GrabCut to the tensor space, and uses the Kullback-Leibler (KL) divergence instead of the usual Riemannian geometry. Lastly, an iteration convergence criterion is proposed to reduce the time of the iteration of GrabCut algorithm dramatically with satisfied segmentation accuracy. After conducting a large number of experiments on synthetic texture images and natural images, the results demonstrate that this method has a more accurate segmentation effect. ? 2017 The Journal of China Universities of Posts and Telecommunications