Bounding-Box Based Segmentation with Single Min-cut Using Distant Pixel Similarity
This paper addresses the problem of interactive image segmentation with a user-supplied object bounding box. The underlying problem is the classification of pixels into foreground and background, where only background information is provided with sample pixels. Many approaches treat appearance models as an unknown variable and optimize the segmentation and appearance alternatively, in an expectation maximization manner. In this paper, we describe a novel approach to this problem: the objective function is expressed purely in terms of the unknown segmentation and can be optimized using only one minimum cut calculation. We aim to optimize the trade-off of making the foreground layer as large as possible while keeping the similarity between the foreground and background layers as small as possible. This similarity is formulated using the similarities of distant pixel pairs. We evaluated our algorithm on the GrabCut dataset and demonstrated that high-quality segmentations were attained at a fast calculation speed.