Superpixel Segmentation with Adaptive Nonlocal Random Walk

2018 2nd International Conference on Artificial Intelligence Applications and Technologies

计算机科学

Wang, Hui^1

Beijing Laboratory of Intelligent Information Technology, School of Computer Science, Beijing Institute of Technology, Beijing, China^1

关键词: Gradient-based method;    Nonlocal;    Random Walk;    Random walk modeling;    Seed point;    State-of-the-art methods;    Superpixel segmentations;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/435/1/012001/pdf
DOI  :  10.1088/1757-899X/435/1/012001

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

In this paper, we propose a novel superpixel segmentation method using adaptive nonlocal random walk (ANRW) algorithm. There are three main steps in our image superpixel algorithm. Our method is based on the random walk model, and 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 nonlocal random walk (NRW) to be more suitable for image segmentation. In the last step, we merge these small superpixels to get the final regular and compact superpixels. The experimental results have demonstrated that our method achieves better performance than the state-of-the-art methods.