Visual data mining of multimedia data for social and behavioral studies
Information Visualization
In situ visualization at extreme scale: challenges and opportunities
IEEE Computer Graphics and Applications
Chrono-gait image: a novel temporal template for gait recognition
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Information theory in computer graphics and visualization
SIGGRAPH Asia 2011 Courses
Importance Driven Automatic Color Design for Direct Volume Rendering
Computer Graphics Forum
Generating time lines with virtual words for time-varying data visualization
Proceedings of the 5th International Symposium on Visual Information Communication and Interaction
An exploratory technique for coherent visualization of time-varying volume data
EuroVis'10 Proceedings of the 12th Eurographics / IEEE - VGTC conference on Visualization
Semi-automatic time-series transfer functions via temporal clustering and sequencing
EuroVis'09 Proceedings of the 11th Eurographics / IEEE - VGTC conference on Visualization
Visual exploration of time-series data with shape space projections
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
Visualization and analysis of 3D time-varying simulations with time lines
Journal of Visual Languages and Computing
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The ability to identify and present the most essential aspects of time-varying data is critically important in many areas of science and engineering. This paper introduces an importance-driven approach to time-varying volume data visualization for enhancing that ability. By conducting a block-wise analysis of the data in the joint feature-temporal space, we derive an importance curve for each data block based on the formulation of conditional entropy from information theory. Each curve characterizes the local temporal behavior of the respective block, and clustering the importance curves of all the volume blocks effectively classifies the underlying data. Based on different temporal trends exhibited by importance curves and their clustering results, we suggest several interesting and effective visualization techniques to reveal the important aspects of time-varying data.