DenseCut: Densely Connected CRFs for Realtime GrabCut

Ming-Ming Cheng    Victor Adrian Prisacariu   Shuai Zheng    Philip H. S. Torr   Carsten Rother

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 high-quality 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 that still achieves very high-quality results. We propose to replace the time consuming iterative refinement of global color models in traditional GrabCut formulation by a densely connected CRF. To motivate this decision, we show that a dense CRF implicitly models unnormalized global color models for foreground and background. Such a 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 considerably higher accuracy.

Paper

1. DenseCut: Densely Connected CRFs for Realtime GrabCut. Ming-Ming Cheng, Victor Adrian Prisacariu, Shuai Zheng, Philip H. S. Torr, Carsten Rother. Computer Graphics Forum, 2015. [Project page] [pdf] [slides] [bib] [c++]

Datasets

1. The GrabCut dataset used in this paper is no longer maintained by its original research team (GrabCut – Microsoft Research). We provide an alternative download link.
2. Here is another binary segmentation dataset: MSRA10K.