DenseCut: Densely Connected CRFs for Realtime GrabCut

Abstract

Figure-ground segmentation from bounding box input, provided either automatically or manually, has been extremely popular in the last decade and influenced various applications. A lot of research has focused on highquality segmentation, using complex formulations which often lead to slow techniques, and often hamper practical usage. In this paper we demonstrate a very fast segmentation technique which still achieves very high quality results. We propose to replace the time consuming iterative refinement of global colour models in traditional GrabCut formulation by a densely connected CRF. To motivate this decision, we show that a dense CRF implicitly models unnormalized global colour models for foreground and background. Such relationship provides insightful analysis to bridge between dense CRF and GrabCut functional. We extensively evaluate our algorithm using two famous benchmarks. Our experimental results demonstrated that the proposed algorithm achieves an order of magnitude (10 ) speed-up with respect to the closest competitor, and at the same time achieves a considerably higher accuracy.