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FeatureNet: automatic visual summarization of major features in multivariate volume data

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Abstract

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.

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Acknowledgements

The authors would like to thank the anonymous reviewers for their valuable comments. This work was supported by the National Key Research & Development Program of China (2017YFB0202203), National Natural Science Foundation of China (61472354 and 61672452), NSFC-Guangdong Joint Fund (U1611263), and the Fundamental Research Funds for the Central Universities.

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Authors and Affiliations

  1. State Key Lab of CAD&CG, Zhejiang University, Hangzhou, China

    Qirui Wang, Yubo Tao & Hai Lin

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  1. Qirui Wang
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  2. Yubo Tao
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  3. Hai Lin
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Correspondence to Yubo Tao or Hai Lin.

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Wang, Q., Tao, Y. & Lin, H. FeatureNet: automatic visual summarization of major features in multivariate volume data. J Vis 21, 443–455 (2018). https://doi.org/10.1007/s12650-017-0459-x

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