  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.
