Fine-structured object segmentation via local and nonlocal neighborhood propagation
Yongchao Gong, Shiming Xiang, Lingfeng Wang, Chunhong Pan · 2015
In this paper, we present a novel method for the challenging task of fine-structured (FS) object segmentation. This task is formulated as a label propagation problem on an affinity graph. To enhance the completeness and connectivity of the FS objects, we introduce a novel neighborhood system combining both local and nonlocal connections, together with a robust scheme for edge weight calculation. Additionally, region cost is incorporated into the energy function to further maintain the connectivity of fine parts where the propagation is hard to reach. An appealing advantage of the proposed method is that the energy minimization has a closed-form solution and global optimum is guaranteed. Comparative experimental results on three datasets demonstrate the effectiveness of the proposed method.