自适应随机游走图像分割算法

An Adaptive Random Walk Algorithm for Image Segmentation

传统的随机游走算法图像信息描述单一,目标轮廓易受背景干扰;针对这一问题,提出一种自适应随机游走图像分割算法.算法首先建立了一种基于纹理相似性的权函数表达式,借助Gabor能量滤波器,首次将纹理特征引入到随机游走算法中,来突出图像的结构信息;其次,为了更加准确地计算节点间的连接权值,算法还提出一种自适应权值计算方法,根据图像边缘密度,自适应地计算纹理和灰度特征在权函数中所占的权重.最后应用狄利克雷边界条件,实现图像分割.实验结果表明,所提算法更好地刻画了图像的结构信息;与传统方法相比,具有更好的适用性和分割准确性.

To solve the problems that the description of image information is simple and the outline of the objective is easily influenced by background disturbances,an adaptive random walk（RW） image segmentation algorithm is proposed.A texture-based similarity weight expression is given,with the texture features introduced into RW algorithm for the first time to highlight the image structural information.In order to accurately calculate the weight between two adjacent nodes,an adaptive weight expression is proposed,i.e.,the proportion of intensity-based and texture-based weights in weight expression will be adaptively calculated according to the image edge density.High-quality segmentation results can be achieved by solving Dirichlet boundary condition.The experiments demonstrates that the proposed algorithm accurately describes image structural information and is more applicable and accurate in comparison with graph cut（GC） and typical RW algorithms.