论文标题
编辑合并树之间的距离
Edit Distance between Merge Trees
论文作者
论文摘要
诸如合并树之类的拓扑结构提供了标量场的抽象和简洁的表示。它们促进了有效的可视化和互动式探索功能丰富的数据。合并树在标量字段中捕获了子级和超级套件的拓扑。估计合并树之间的相似性是一个重要的问题，即具有定向时间变化数据可视化的应用程序。我们提出了一种基于树编辑距离的方法，以比较合并树。比较度量满足度量属性，可以有效地计算，并且编辑操作的成本模型既直观又捕获了合并树的众所周知的属性。随时间变化标量场，3D冷冻电子显微镜数据，形状数据和各种合成数据集的实验结果显示，编辑距离对标量场的特征驱动分析的实用性。
Topological structures such as the merge tree provide an abstract and succinct representation of scalar fields. They facilitate effective visualization and interactive exploration of feature-rich data. A merge tree captures the topology of sub-level and super-level sets in a scalar field. Estimating the similarity between merge trees is an important problem with applications to feature-directed visualization of time-varying data. We present an approach based on tree edit distance to compare merge trees. The comparison measure satisfies metric properties, it can be computed efficiently, and the cost model for the edit operations is both intuitive and captures well-known properties of merge trees. Experimental results on time-varying scalar fields, 3D cryo electron microscopy data, shape data, and various synthetic datasets show the utility of the edit distance towards a feature-driven analysis of scalar fields.
