Data-guided random walks for fine-structured object segmentation

Yongchao Gong, Shiming Xiang, Chunhong Pan

Abstract: Random walks (RW) is a popular technique for object segmentation. Apart from the satisfactory performance in various applications, its most appealing advantage is the computational efficiency. However, RW often fails to produce complete and connected results in fine-structured (FS) object segmentation. To utilize the high efficiency and overcome the drawbacks in tackling FS objects, we develop a novel approach within the RW framework. Specifically, we propose to introduce labeling preference learned from the image data into the RW model to guide the propagation of random walkers. With the help of the guidance, random walkers are more likely to propagate correctly to the FS regions, thus yielding more accurate results. Similar to RW, this approach also bears properties such as computational efficiency, closed-form solution and unique global optimum. Moreover, it has the capacities of handling disconnected objects and transferring segmentation. Comparative experimental results demonstrate that the proposed approach achieves the state-of-the-art performance in FS object segmentation, with a low requirement of runtime.