LAUSR

Multivariate volume data sets usually have complex interactions between fields, and features from different fields, such as segmented regions and isosurfaces, can be associated together to intuitively reveal the correlation and difference between fields. In this paper, we present a visual analytic approach for interactive feature exploration. A graph-based representation, called FeatureNet, is designed to provide a full picture of major features extracted from each field. FeatureNet visually summarizes both the nesting and association relationships of major features in each variable, and serves as a navigation tool to guide data exploration. Case studies with three simulation data sets demonstrate the effectiveness and usefulness of FeatureNet, and it can help users better understand and inspect the nesting and correlation relationships between fields.Graphical Abstract