Interactive techniques for extracting the foreground object from an image have been the interest of research in computer vision for a long time. This paper addresses the problem of an efficient, semi-interactive extraction of a foreground object from an image. Snake (also known as Active contour) and GrabCut are two popular techniques, extensively used for this task. Active contour is a deformable contour, which segments the object using boundary discontinuities by minimizing the energy function associated with the contour. GrabCut provides a convenient way to encode color features as segmentation cues to obtain foreground segmentation from local pixel similarities using modified iterated graph-cuts. This paper first presents a comparative study of these two segmentation techniques, and illustrates conditions under which either or both of them fail. We then propose a novel formulation for integrating these two complimentary techniques to obtain an automatic foreground object segmentation. We call our proposed integrated approach as “SnakeCut”, which is based on a probabilistic framework. To validate our approach, we show results both on simulated and natural images.
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