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.

翻译：测地线模型被公认为解决各类图像分割问题的有效工具。现有方法大多仅利用局部点状图像特征来追踪目标边界的测地路径。然而此类分割策略无法顾及图像边缘特征的连通性，在复杂场景下极易引发捷径问题。本文提出一种基于最小测地线框架的新型图像分割模型，该模型融合了自适应切割式圆形最优路径计算方案与图结构边界提议分组机制。具体而言，自适应切割可断开图像域，迫使目标轮廓仅能单次穿越该切割线。边界提议由预计算的图像边缘片段构成，为分割模型提供连通性先验信息。这些边界提议随后被整合至所提分割模型中，使得目标分割轮廓由选定的边界提议集合及其连接测地路径共同构成。实验结果表明，所提模型在性能上显著优于现有基于最小路径的图像分割方法。