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Video object segmentation is a critical task in multimedia analysis and editing. Normally, the user provides some hints of foreground and background, then the target object is extracted from the video sequence. Most previous methods are either computation-expensive or labor-intensive, and approaches that assume static background have limited applications. In this letter, we propose a novel video segmentation system that integrates Markov random field-based contour tracking with graph-cut image segmentation. The contour tracking propagates the shape of the target object, whereas the graph-cut refines the shape and improves the accuracy of video segmentation. Experimental results show that our segmentation system is efficient and requires less key-frames and user interactions.