Mean Shift Based Random Walker Interactive Image Segmentation Algorithm

A Mean Shift based random walker interactive image segmentation algorithm is proposed to solve the problems that the objective contour is prone to the influence of the natural texture background and computation speed is low.Firstly,image is segmented into many small homogeneous regions by Mean Shift pre-segmentation algorithm,and the homogeneous regions are used to build an undirected graph,instead of pixels.Color histogram is used as a descriptor to represent the region color feature statistics,and Euclidean distance and Gaussian weighting function are used to describe the similarity of adjacent regions.Finally,the discrete potential theory is used to calculate the potential of each node in the graph,and the final image segmentation can be achieved according to the greatest potential of each node in the graph.The results of experiments demonstrate that the segmentation accuracy and efficiency of our proposed method is improved significantly comparing with traditional random walker algorithms.