Efficient video object segmentation by graph-cut

Segmentation of video objects from background is a popular computer vision topic and has many important applications. Most existing methods are either computationally expensive or requiring manual initialization, static cameras, and/or rigid scenes. In a previous work, we proposed a joint spatio-temporal linear regression algorithm to automatically cluster the sparse edge/corner pixels in each video frame and obtain two motion models for the object and background respectively. To label the rest pixels for object segmentation, in this paper, we propose to model the Optical-Flow residual error, color intensity residual error and temporal label consistency features, as well as color/edge orientation consistency constrains, in a graph, and apply the GraphCut algorithm to minimize the energy of the graph to obtain an optimal segmentation of the two motion layers boundaries. Finally the object layer is identified from the two using simple heuristics. Experimental segmentation result with videos taken by webcams is promising.