Image Segmentation Based on Visual Saliency and Graph Cuts

Graph Cuts algorithm is a classical and effective method for image segmentation.However,it tends to produce segmentation errors and shrinking bias when the foreground and background color distributions overlap,and its interactivity is less efficient.To address these problems an interactive method for image segmentation based on visual saliency and graph cuts is proposed in this paper.First the Mean Shift algorithm is applied to efficiently pre-segment the original image into regions to construct region adjacency graph,which can greatly reduce the computational complexity.Then with image saliency analysis the reliability of the data item constraint is enhanced,and by using local adaptive regularization parameter the shrinking bias problem is improved effectively.The experiments demonstrate the superior performance of the proposed method in terms of interactive efficiency and segmentation accuracy.