Deep interactive image segmentation based on region and Boundary-click guidance

•Region and boundary clicks are used to be a user interaction strategy.•An interactive two-stream network structure is used to learn the region and boundary features of interest.•An information diffusion module is used to propagate the region and boundary-click labels. In interactive image segmentation, the target object of interest can be extracted based on the guidance of user interactions. One of the main goals in this task is to reduce the user interaction burden and ensure satisfactory segmentation with as few interactions as possible. Thanks to the development of deep learning technology, neural network-based interactive approaches have significantly improved the segmentation performance through powerful feature representation. Only limited point (click) interaction is required for the user to complete the segmentation. This paper mainly follows the deep learning-based interactive segmentation methods and explores more efficient interaction strategies and effective segmentation models. We further simplify user interaction to two clicks, where the first click is utilized to select the target region and the other aims to determine the target boundary. Based on the region and boundary clicks, an interactive two-stream network structure is naturally derived to learn the region and boundary features of interest. Furthermore, we also construct an information diffusion module to better propagate the region and boundary-click labels, which helps to enhance the similarity within the region and the discrimination between boundaries. Vast experiments on the popular GrabCut, Berkeley, DAVIS, MS COCO and SBD datasets verified the effectiveness of the proposed method.