  Binary image segmentation plays an important role in computer vision and has
been widely used in many applications such as image and video editing, object
extraction, and photo composition. In this paper, we propose a novel
interactive binary image segmentation method based on the Markov Random Field
(MRF) framework and the fast bilateral solver (FBS) technique. Specifically, we
employ the geodesic distance component to build the unary term. To ensure both
computation efficiency and effective responsiveness for interactive
segmentation, superpixels are used in computing geodesic distances instead of
pixels. Furthermore, we take a bilateral affinity approach for the pairwise
term in order to preserve edge information and denoise. Through the alternating
direction strategy, the MRF energy minimization problem is divided into two
subproblems, which then can be easily solved by steepest gradient descent (SGD)
and FBS respectively. Experimental results on the VGG interactive image
segmentation dataset show that the proposed algorithm outperforms several
state-of-the-art ones, and in particular, it can achieve satisfactory
edge-smooth segmentation results even when the foreground and background color
appearances are quite indistinctive.
