Fast Interactive Image Segmentation Using Bipartite Graph Based Random Walk with Restart

Yunfan Du, Fei Li, Rujie Liu

Abstract: Although random walk with restart(RWR) has been successfully used in interactive image segmentation, the traditional implementation of RWR does not scale for large images. As the images are usually stored on local disk prior to user interaction, we can preprocess the images to save user time. In this paper, we do an offline precomputation that over-segments the input image into superpixels with different scales and then aggregates superpixels and pixels into one bipartite graph which fuses the high level and low level information. Given user scribbles, we do a realtime RWR on the bipartite graph by applying an approximate method which maps the RWR from pixel level to superpixel level. As the number of superpixels is far more less than the number of pixels in the image, our method reduces the amount of user time significantly. The experimental results demonstrate that our method achieves a similar result compared to original RWR along with outperforming in speed.