In this work, we propose trait-based merge trees a generalization of merge trees to feature level sets, targeting the analysis of tensor field or general multi-variate data. For this, we employ the notion of traits defined in attribute space as introduced in the feature level sets framework. The resulting distance field in attribute space induces a scalar field in the spatial domain that serves as input for topological data analysis. The leaves in the merge tree represent those areas in the input data that are closest to the defined trait and thus most closely resemble the defined feature. Hence, the merge tree yields a hierarchy of features that allows for querying the most relevant and persistent features. The presented method includes different query methods for the tree which enable the highlighting of different aspects. We demonstrate the cross-application capabilities of this approach with three case studies from different domains.

提出了基于特征的合并树，将合并树推广到特征级别集，目标是分析张量场或一般多变量数据；通过属性空间中的特征概念来生成属性空间中的距离场，从而引出空间域中的标量场，用于拓扑数据分析，合并树中的叶子表示输入数据中与定义的特征最接近且最相似的区域，因此合并树提供了一种特征的层次结构，允许查询最相关和最持久的特征；该方法包括对树的不同查询方法，以突出不同的方面；通过三个不同领域的案例研究证明了该方法的跨应用能力。

基于特征诱导的合并树的多领域可视化