KnifeCut: Refining Thin Part Segmentation with Cutting Lines

Zheng Lin, Zheng-Peng Duan, Zhao Zhang, Chun-Le Guo, Ming-Ming Cheng

Abstract: Objects with thin structures remain challenging for current image segmentation techniques. Their outputs often do well in the main body but with thin parts unsatisfactory. In practical use, they inevitably need post-processing. However, repairing them is time-consuming and laborious, either in professional editing applications (e.g. PhotoShop) or by current interactive image segmentation methods (e.g. by click, scribble, and polygon). To refine the thin parts for unsatisfactory pre-segmentation, we propose an efficient interaction mode, where users only need to draw a line across the mislabeled thin part like cutting with a knife. This low-stress and intuitive action does not require the user to aim deliberately, and is friendly when using the mouse, touchpad, and mobile devices. Additionally, the line segment provides a contrasting prior because it passes through both the foreground and background regions and there must be thin part pixels on it. Based on the interaction idea, we propose KnifeCut, which offers the users two results, where one only focuses on the target thin part and the other provides the refinements for all thin parts that share similar features with the target one. To our best knowledge, KnifeCut is the first method to solve interactive thin structure refinement pertinently. Extensive experiments and visualized results further demonstrate its friendliness, convenience, and effectiveness. The project page is available on http://mmcheng.net/knifecut/.