GridIIS: Grid Based Interactive Image Segmentation

Interactive segmentation enables users to specify the object of interest (OOI) via various interaction strategies to obtain accurate segmentation results. An ideal interactive method should efficiently and accurately express users' segmentation intentions. However, the existing methods can only use a single interactive mode, ignoring the differences in scale and shape between OOIs, resulting in an inflexibility labeling process. In this paper, we propose a grid-based interactive image segmentation method (GridIIS). Specifically, GridIIS overlays grids on the image, and users can specify the location and shape of the OOI by selecting the grid areas as the interactive guidance. Users can choose the appropriate grid selection method and size considering the OOI's scale, shape, and boundary clarity to obtain guidance. We accordingly propose a novel grid sampling strategy, that considers the OOI's scale and shapes to adaptively estimate the grid size and area. Experiments on several datasets from different domains (street views, medical images, scene texts, etc.) show that our method achieves superior performance with fewer interaction rounds and exhibits strong generalization ability in cross-domain datasets.