Static correlation visualization for large time-varying volume data

Cheng Kai Chen, Chaoli Wang, Kwan-Liu Ma, Andrew T. Wittenberg

Research output: Chapter in Book/Report/Conference proceedingConference contribution

31 Scopus citations


Finding correlations among data is one of the most essential tasks in many scientific investigations and discoveries. This paper addresses the issue of creating a static volume classification that summarizes the correlation connection in time-varying multivariate data sets. In practice, computing all temporal and spatial correlations for large 3D time-varying multivariate data sets is prohibitively expensive. We present a sampling-based approach to classifying correlation patterns. Our sampling scheme consists of three steps: selecting important samples from the volume, prioritizing distance computation for sample pairs, and approximating volume-based correlation with sample-based correlation. We classify sample voxels to produce static visualization that succinctly summarize the connection among all correlation volumes with respect to various reference locations. We also investigate the error introduced by each step of our sampling scheme in terms of classification accuracy. Domain scientists participated in this work and helped us select samples and evaluate results. Our approach is generally applicable to the analysis of other scientific data where correlation study is relevant.

Original languageEnglish (US)
Title of host publicationIEEE Pacific Visualization Symposium 2011, PacificVis 2011 - Proceedings
Number of pages8
StatePublished - May 11 2011
Event4th IEEE Pacific Visualization Symposium 2011, PacificVis 2011 - Hong Kong, China
Duration: Mar 1 2011Mar 4 2011


Other4th IEEE Pacific Visualization Symposium 2011, PacificVis 2011
CityHong Kong

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


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