【摘要】 传统随机游走图像分割需要多次交互设置种子点以获得理想的分割结果。在视觉注意的基础上,提出了一种新的自动确定种子点的随机游走图像分割算法。首先对图像进行超像素分割,并生成概率边界图(PBM);然后基于Itti模型,通过视觉注意焦点的转移搜寻待分割的关键区域;为确定关键分割区域种子点,以当前注意焦点作为极点对概率边界图进行极坐标变换,在获得的极坐标概率边界图上建立关于焦点区域边界的能量函数,采用图论max-flow min-cut算法最小化能量函数检测焦点区域的最优边界,焦点区域边界内的超像素即为种子点;最后以超像素为节点构造图,在图上随机游走完成图像分割。在Berkeley Segmentation Data Set上的实验表明本文方法能有效分割复杂图像。更多还原

【Abstract】 The traditional random walks based image segmentation algorithm requires setting seed points interactively to obtain the desired segmentation results. Based on visual attention,the paper proposes a new random walks based image segmentation algorithm with the seed points determined automatically. Firstly,the probability boundary map( PBM) is generated and the image is divided into super pixels. Then,the key segmentation region is searched by shifting visual attention focus with Itti model. In order to determine the seed points of the key segmentation region,the probabilistic boundary map is transformed into polar coordinates map taking the current focus of attention as the pole. The energy function about the boundary of the focal region is established on the obtained polar coordinate probabilistic boundary map. The energy function can be minimized by the max-flow min-cut algorithm,and the super pixels within the boundary of focal region are the seed points of segmentation region. Finally,super pixels of images are used as nodes to construct a graph,random walks algorithm is conducted on the graph to complete the image segmentation. The experiments on Berkeley Segmentation Data Set show that the proposed method is effective to complex images’ segmentation.更多还原