Fine-structured object segmentation via edge-guided graph cut with interaction simplification

Yongchao Gong, Shiming Xiang, Lingfeng Wang, Chunhong Pan

Abstract: Fine-structured object segmentation is a challenging problem in object segmentation community. There are mainly two difficulties that can seriously degrade the segmentation quality: 1) insufficient interactions on fine structures due to the high demand of time and manual efforts, and 2) shrinking bias that discourages long object boundaries. To address these two issues, we develop a novel method within the graph cut framework. First, the commonly used operation of scribbling or dragging bounding boxes is replaced by loosely drawing a few rectangles, thus the interaction burden is largely reduced. Second, an edge-guided graph cut model is proposed to mitigate shrinking bias. This model enforces connectivity of fine structures by adjusting the weighting between neighboring pixels. Finally, the segmentation task is formulated as an optimization problem, which can be optimized effectively and efficiently. Comparative experimental results demonstrate the effectiveness of our method.