This paper proposes a new approach for superpixel segmentation. It is formulated as finding a rooted spanning forest of a graph with respect to some roots and a path-cost function. The underlying graph represents an image, the roots serve as seeds for segmentation, each pixel is connected to one seed via a path, the path-cost function measures both the color similarity and spatial closeness between two pixels via a path, and each tree in the spanning forest represents one superpixel. Originating from the evenly distributed seeds, the superpixels are guided by a path-cost function to grow uniformly and adaptively, the pixel-by-pixel growing continues until they cover the whole image. The number of superpixels is controlled by the number of seeds. The connectivity is maintained by region growing. Good performances are assured by connecting each pixel to the similar seed, which are dominated by the path-cost function. It is evaluated by both the superpixel benchmark and supervoxel benchmark. Its performance is ranked as the second among top performing state-of-the-art methods. Moreover, it is much faster than the other superpixel and supervoxel methods.

中文翻译：

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有根跨越超像素

本文提出了一种新的超像素分割方法。它被公式化为找到关于一些根和路径成本函数的图的有根生成森林。底层图表示图像，根作为分割的种子，每个像素通过路径连接到一个种子，路径成本函数通过路径测量两个像素之间的颜色相似性和空间接近度，并且每棵树生成森林代表一个超像素。源自均匀分布的种子，超像素在路径成本函数的引导下均匀且自适应地增长，逐个像素地继续增长，直到它们覆盖整个图像。超像素的数量由种子数量控制。连通性是由区域增长维持的。通过将每个像素连接到由路径成本函数主导的相似种子，可以确保良好的性能。它由超像素基准和超体素基准进行评估。它的性能在性能最好的最先进方法中排名第二。此外，它比其他超像素和超体素方法要快得多。