Interactive Binary Image Segmentation with Edge Preservation
This work addresses interactive segmentation for computer vision applications like image editing, offering incremental improvements in edge preservation and efficiency.
The authors tackled interactive binary image segmentation by proposing a method based on Markov Random Fields and fast bilateral solver, which outperformed state-of-the-art methods on the VGG dataset, achieving satisfactory edge-smooth results even with indistinct foreground and background colors.
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.