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.

中文翻译：

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用于图像超像素分割的自适应非局部随机游走

在本文中，我们提出了一种使用自适应非局部随机游走 (ANRW) 算法的新型超像素分割方法。我们的图像超像素分割算法主要分为三个步骤。我们的方法基于随机游走模型，其中在第一步中通过基于梯度的方法产生种子点以生成初始超像素。第二步，提出ANRW，通过调整NRW得到初始超像素，以获得更好的图像和超像素分割。最后一步，将这些小的超像素合并，得到最终的规则紧凑的超像素。实验结果表明，我们的方法比最先进的方法实现了更好的超像素性能。我们的源代码可在：http://github.com/shenjianbing/ANRW 获得。