论文标题
通过一致的细分评估层次结构
Assessing hierarchies by their consistent segmentations
论文作者
论文摘要
当前的通用分割方法首先创建嵌套图像分区的层次结构，然后从中指定分割。 我们的第一个贡献是描述几种方法，其中大多数是使用层次结构元素来指定分割的方法。然后，我们考虑由有限数量的层次结构元素指定的最佳层次结构诱导的分割。 我们专注于二进制分割的共同质量度量，即Jaccard指数（也称为IOU）。 优化JACCARD指数是高度不平凡的，但是我们提出了一种有效的方法来做到这一点。这样，我们就可以在层次结构创建的任何细分的质量上获得算法独立的上限。我们发现，可获得的分割质量差异很大，具体取决于层次结构元素指定段的方式，并且通常可以使用几个层次结构元素来表示分割。 （代码可用）。
Current approaches to generic segmentation start by creating a hierarchy of nested image partitions and then specifying a segmentation from it. Our first contribution is to describe several ways, most of them new, for specifying segmentations using the hierarchy elements. Then, we consider the best hierarchy-induced segmentation specified by a limited number of hierarchy elements. We focus on a common quality measure for binary segmentations, the Jaccard index (also known as IoU). Optimizing the Jaccard index is highly non-trivial, and yet we propose an efficient approach for doing exactly that. This way we get algorithm-independent upper bounds on the quality of any segmentation created from the hierarchy. We found that the obtainable segmentation quality varies significantly depending on the way that the segments are specified by the hierarchy elements, and that representing a segmentation with only a few hierarchy elements is often possible. (Code is available).
