Multi-field Visualization: Trait design and trait-induced merge trees

TL;DR

This paper introduces trait-induced merge trees (TIMTs) for analyzing multi-field data, simplifying trait design with Cartesian decomposition and automatic point trait suggestions, and providing a hierarchical feature analysis tool validated across multiple case studies.

Contribution

It proposes trait-induced merge trees (TIMTs) for topological analysis of multi-variate data, with a novel trait design method and automatic trait suggestion, enhancing feature selection and interpretability.

Findings

TIMTs effectively analyze multi-field data topologically.

Cartesian decomposition simplifies trait design.

Automatic point trait suggestion improves feature identification.

Abstract

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.