A new circular minimal path model for interactive image segmentation
Liu Li, Weijie Zhang, Da Chen, Minglei Shu · 2024
Minimal paths are widely used in the image segmentation problems. Most existing methods only exploit local point-wise image features to track minimal paths for delineating the boundaries, increasing the risk of shortcuts especially in the case of complicated scenario. In this work, we introduce a new circular minimal path model invoking a graph-based boundary proposals grouping scheme and an adaptive cut for interactive image segmentation. The boundary proposals are comprised of edge segments, incorporating the nonlocal connectivity information into the proposed model. The target contours are made up of boundary proposals and minimal paths selected by a graph-based optimization way. The adaptive cut can disconnect the image domain such that the target contours are imposed to pass through this cut only once, allowing to deal with a great variety of segmentation tasks. The effectiveness of the proposed model has been validated on image segmentation tasks.