Adaptive Nonlocal Random Walks for Image Superpixel Segmentation

In this paper, we propose a novel superpixel segmentation method using an adaptive nonlocal random walk (ANRW) algorithm. There are three main steps in our image superpixel segmentation algorithm. Our method is based on the random walk model, in which the seed points are produced to generate the initial superpixels by a gradient-based method in the first step. In the second step, the ANRW is proposed to get the initial superpixels by adjusting the NRW to obtain a better image and superpixel segmentation. In the last step, these small superpixels are merged to get the final regular and compact superpixels. The experimental results demonstrate that our method achieves a better superpixel performance than the state-of-the-art methods. Our source code will be available at: http://github.com/shenjianbing/ANRW.