A BS process involves building a model of the background and extracting regions of the foreground (moving objects) with the assumptions that the camera remains stationary and there exist no movements in the background. Video object extraction is a critical task in multimedia analysis and editing. Normally, the user provides some hints of foreground and background, and then the target object is extracted from the video sequence. In this paper, we propose a object segmentation system that integrates a clustering model with Markov random field-based contour tracking and 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.