【摘要】 传统的Grab Cut图像分割方法大多基于图像的像素值建立图模型,未考虑到彩色图像中丰富的纹理信息。为此,提出一种新的Grab Cut模型图像分割算法。对比基于结构张量的Grab Cut分割方法和传统Grab Cut分割方法的结果,利用结构张量和像素值构建紧缩的结构张量。为提高计算的简洁性和高效性,将Grab Cut方法构建的混合高斯模型扩展到张量空间,并用Kullback-Leible散度代替常用的黎曼度量。在合成纹理图像和自然图像上进行的实验结果表明,与Carsten Rother,GACWRF等方法相比,该算法具有更精确的分割效果,不仅实现了纹理信息与颜色信息的无参融合,而且提高了计算效率。更多还原

【Abstract】 Traditional Grab Cut based image segmentation method is mainly based on the image pixel values to build a graph model,and does not take into account the rich texture of color image information. This paper presents an image segmentation algorithm based on Grab Cut model,and contrasts results of Structure Tensor( ST) Grab Cut segmentation method and traditional Grab Cut segmentation method. The method uses the ST and the pixel values to construct the tight ST. For concise and efficient calculation,this paper extends Gaussian Mixture Model( GMM) built based on Grabcut method to tensor space,and uses Kullback-Leible( KL) divergence instead of the commonly used the Riemannian metric.Through a lot of experiments on synthetic texture images and natural images,results showthat,compared with carstem Rother,GACWRF method the algorithm has more accurate segmentation effects,not only achieves the texture and color information parameter fusion,but also improves the computational efficiency.更多还原