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) lorenametaute.com 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 here 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.