Object Segmentation Based on Contour-Skeleton Duality

This paper presents a novel algorithm for performing integrated object segmentation from a single image. Unlike other state of the art methods which focus on either using contour-based or skeleton-based methods, our approach considers the duality of the two representations (contour/skeleton) and an iterative segmentation procedure that alternates between contour recovery and skeleton fitting. The contour recovery extracts the object contour by adopting the skeleton prior, while the skeleton fitting employs the contour to infer the optimal representation of the object shape. In our approach, the object contour can be directly recovered with no iteration if a detected skeleton is given. Although the proposed method is evaluated for human pose segmentation experiments, it can also be applied to other applications.