基于颜色和纹理信息的快速前景提取方法

近年来,研究者们提出了许多算法来处理前景提取和图像抽取问题.然而,这些算法存在许多共同缺点:需要三元图作为输入、计算时间过长、大部分算法仅仅使用颜色信息等等.在这篇文章里,作者提出了一种新的快速多层次前景提取办法.首先,应用一种改进的多层次图分割算法,将输入图像粗略地分割为前景和背景两个部分.然后,使用信念传播算法(belief propagation)估计前景/背景交界处像素的不透明度.不同于通常的信念传播算法,在平滑项和颜色项之外,作者通过构造灰度共生矩阵引人了纹理信息.鉴于数码相机图像的分辨率仍在持续快速增长,作者提出的多层次图分割算法可以在加速上述计算过程的同时,获得可以和当前许多算法相媲美的局部最优解.实验结果证明文中所提出的算法对于大尺寸图像尤其有效.

A Fast Object Extraction Method Based on Color and Texture Information

In recent years researchers have developed many algorithms for object extraction and image matting. However, previous approaches usually require trimaps as input, or consume in-tolerably long time to get the final results, and most of them just consider the color information. This paper proposes a novel fast hierarchical object extraction method. First the input image is segmented roughly into two regions: foreground and background, using a modified hierarchical Graph Cuts algorithm. After that, the opacity values for the pixels nearby the foreground/back-ground border are estimated using belief propagation (BP). Unlike traditional BP-based approa-ches, besides the smoothness and color constraints, the texture information is introduced by building grayscale co-occurrence matrices. Moreover, considering the fact that the resolution of photographs taken by digital cameras continues to increase at a rapid and steady pace, the modi-fied version of hierarchical Graph Cuts proposed in this paper could accelerate the above-men-tioned computation process, getting a comparably satisfactory local optimal solution as previous ap-proaches. Experiments show that the method is effective and efficient especially for large images.