Superpixel segmentation has become an important research problem in image processing. In this paper, we propose an Iterative Spanning Forest (ISF) framework, based on sequences of Image Foresting Transforms, where one can choose i) a seed sampling strategy, ii) a connectivity function, iii) an adjacency relation, and iv) a seed pixel recomputation procedure to generate improved sets of connected superpixels (supervoxels in 3D) per iteration. The superpixels in ISF structurally correspond to spanning trees rooted at those seeds. We present five ISF methods to illustrate different choices of its components. These methods are compared with approaches from the state-of-the-art in effectiveness and efficiency. The experiments involve 2D and 3D datasets with distinct characteristics, and a high level application, named sky image segmentation. The theoretical properties of ISF are demonstrated in the supplementary material and the results show that some of its methods are competitive with or superior to the best baselines in effectiveness and efficiency.
翻译:超级像素分解已成为图像处理中的一个重要研究问题。 在本文中, 我们提出一个基于图像森林变异序列的循环覆盖森林框架, 该框架基于图像森林变异的顺序, 其中人们可以选择一) 种子取样战略, (二) 连接功能, (三) 连接功能, (三) 相邻关系, 以及 (四) 种子像素反省程序, 以产生每迭接相接相连接的成套超级像素( 3D 的超级像素) 。 ISF 的超级像素在结构上与根植于这些种子的树木相对应。 我们提出了五种ISF 方法, 以说明其组成部分的不同选择。 这些方法与来自最新技术在有效性和效率方面的做法进行了比较。 这些实验涉及具有不同特性的 2D 和 3D 数据集, 和 高级应用, 名为天空图像分解。 补充材料中展示了 ISF 的理论特性, 结果显示其部分方法在有效性和效率方面与最佳基线具有竞争力或优越性。