Adaptive Optimal Shape Prior for Easy Interactive Object Segmentation

For interactive segmentation approaches, object segmentation in complicated background is cumbersome, and usually needs tedious interactions to refine the incomplete segmentations . In this paper, an adaptive optimal shape prior is proposed for easy interactive object segmentation. Different from the traditional shape priors which only provide loose constraint, our adaptive shape prior gives more accurate and individualized constraint by exploiting the shape information of incomplete segmentation. Moreover, by combining the non-rigid shape registration and a local shape consistency evaluation system presented in this paper, such adaptive optimal shape prior could be achieved automatically. Both of these contributions greatly lighten the burden on users and make interactive segmentation much easier. The comparison experiments on the newly-built TypShape dataset with the related algorithms have demonstrated good performance of the proposed algorithm.