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arXiv 2207.08511cs.CG
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合并树之间的编辑距离

Edit Distance between Merge Trees

Raghavendra Sridharamurthy, Talha Bin Masood, Adhitya Kamakshidasan, Vijay Natarajan

AI总结:

本文提出一种基于树编辑距离的合并树比较方法,满足度量属性且可高效计算,实验证明其在时变标量场等数据的特征驱动分析中具有实用性。

摘要:

诸如合并树之类的拓扑结构提供了标量场的抽象且简洁的表示。它们促进了对特征丰富数据的有效可视化和交互式探索。合并树捕获了标量场中子水平集和超水平集的拓扑。估计合并树之间的相似度是一个重要问题,可应用于时变数据的特征导向可视化。我们提出了一种基于树编辑距离的方法来比较合并树。该比较度量满足度量属性,可高效计算,且编辑操作的成本模型既直观又能捕获合并树的已知特性。在时变标量场、3D冷冻电子显微镜数据、形状数据及多种合成数据集上的实验结果,证明了该编辑距离在标量场特征驱动分析中的实用性。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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