  Geodesic models are known as an efficient tool for solving various image
segmentation problems. Most of existing approaches only exploit local pointwise
image features to track geodesic paths for delineating the objective
boundaries. However, such a segmentation strategy cannot take into account the
connectivity of the image edge features, increasing the risk of shortcut
problem, especially in the case of complicated scenario. In this work, we
introduce a new image segmentation model based on the minimal geodesic
framework in conjunction with an adaptive cut-based circular optimal path
computation scheme and a graph-based boundary proposals grouping scheme.
Specifically, the adaptive cut can disconnect the image domain such that the
target contours are imposed to pass through this cut only once. The boundary
proposals are comprised of precomputed image edge segments, providing the
connectivity information for our segmentation model. These boundary proposals
are then incorporated into the proposed image segmentation model, such that the
target segmentation contours are made up of a set of selected boundary
proposals and the corresponding geodesic paths linking them. Experimental
results show that the proposed model indeed outperforms state-of-the-art
minimal paths-based image segmentation approaches.
