Feature level sets (FLS) have shown significant potential in the analysis of multi-field data by using traits defined in attribute space to specify features in the domain. In this work, we address key challenges in the practical use of FLS: trait design and feature selection for rendering. To simplify trait design, we propose a Cartesian decomposition of traits into simpler components, making the process more intuitive and computationally efficient. Additionally, we utilize dictionary learning results to automatically suggest point traits. To enhance feature selection, we introduce trait-induced merge trees (TIMTs), a generalization of merge trees for feature level sets, aimed at topologically analyzing tensor fields or general multi-variate data. The leaves in the TIMT represent areas in the input data that are closest to the defined trait, thereby most closely resembling the defined feature. This merge tree provides a hierarchy of features, enabling the querying of the most relevant and persistent features. Our method includes various query techniques for the tree, allowing the highlighting of different aspects. We demonstrate the cross-application capabilities of this approach through five case studies from different domains.

翻译：特征水平集（FLS）通过使用属性空间中定义的特征来指定域中的特征，在多场数据分析中展现出显著潜力。本研究解决了FLS实际应用中的两个关键挑战：特征设计与渲染特征选择。为简化特征设计，我们提出将特征笛卡尔分解为更简单的组成部分，使设计过程更直观且计算更高效。此外，我们利用字典学习结果自动推荐点特征。为增强特征选择能力，我们引入特征诱导合并树（TIMT）——这是针对特征水平集的合并树推广形式，旨在对张量场或一般多元数据进行拓扑分析。TIMT中的叶节点表示输入数据中最接近定义特征的区域，因而最能体现定义特征的本质。该合并树提供特征层次结构，支持查询最相关且最稳定的特征。我们的方法包含多种树查询技术，可突显不同层面的特征特性。通过五个跨领域案例研究，我们展示了该方法具有跨学科应用能力。