research-article

Machine Learning to Boost the Next Generation of Visualization Technology

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Kwan-Liu MaAuthors Info & Claims

IEEE Computer Graphics and Applications, Volume 27, Issue 5

Pages 6 - 9

https://doi.org/10.1109/MCG.2007.129

Published: 01 September 2007 Publication History

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Abstract

Many visualization systems do not get widespread adoption because they confront the user with sophisticated operations and interfaces. The author suggests augmenting visualization systems with learning capability to improve both the performance and usability of visualization systems. Several examples including volume segmentation, flow feature extraction, and network security are given illustrating how machine learning can help streamline the process of visualization, simplify the user interface and interaction, and support collaborative work.

References

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A. Hertzmann, "Machine Learning for Computer Graphics: A Manifesto and Tutorial," Proc. Pacific Graphics Conf., IEEE CS Press, 2003, pp. 22–36.

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K.-L. Ma, "Visualizing Visualization: User Interfaces for Managing and Exploring Scientific Visualization Data," IEEE Computer Graphics and Applications, vol. 20, no. 5, 2000, pp. 16–19.

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H. Pfister et al., "The Transfer Function Bake-Off," IEEE Computer Graphics and Applications, vol. 21, no. 3, 2001, pp. 16–23.

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F.-Y. Tzeng, E.B. Lum, and K.-L. Ma, "An Intelligent System Approach to Higher-Dimensional Classification of Volume Data," IEEE Trans. Visualization and Computer Graphics, vol. 11, no. 3, 2005, pp. 273–284.

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F.-Y. Tzeng and K.-L. Ma, "Intelligent Feature Extraction and Tracking for Large-Scale 4D Flow Simulations," Proc. Int'l Conf. High Performance Computing, Networking, Storage and Analysis, IEEE CS Press, 2005.

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C. Muelder, K.-L. Ma, and T. Bartoletti, "A Visualization Methodology for Characterization of Network Scans," Proc. Workshop Visualization for Computer Security (VizSEC), IEEE CS Press, 2005, pp. 29–38.

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F.-Y. Tzeng and K.-L. Ma, "Opening the Black Box—Data Driven Visualization of Neural Networks," Proc. IEEE Visualization Conf., IEEE CS Press, 2005, pp. 383–390.

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F.-Y. Tzeng and K.-L. Ma, "A Cluster-Space Visual Interface for Arbitrary Dimensional Classification of Volume Data," Proc. Joint Eurographics, IEEE TCVG Symp. Visualization, Eurographics Assoc., 2004, pp. 17–24.

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Cited By

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Tkachev GFrey SErtl T(2022)S4: Self-Supervised Learning of Spatiotemporal SimilarityIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2021.310141828:12(4713-4727)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1109/TVCG.2021.3101418
Tkachev GFrey SErtl T(2021)Local Prediction Models for Spatiotemporal Volume VisualizationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2019.296189327:7(3091-3108)Online publication date: 1-Jul-2021
https://dl.acm.org/doi/10.1109/TVCG.2019.2961893
Qu ZZhou YNguyen QCatchpoole D(2019)Using Visualization to Illustrate Machine Learning Models for Genomic DataProceedings of the Australasian Computer Science Week Multiconference10.1145/3290688.3290719(1-8)Online publication date: 29-Jan-2019
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Machine Learning to Boost the Next Generation of Visualization Technology

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cover image IEEE Computer Graphics and Applications

IEEE Computer Graphics and Applications Volume 27, Issue 5

September 2007

95 pages

ISSN:0272-1716

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IEEE Computer Society Press

Washington, DC, United States

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Published: 01 September 2007

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Tkachev GFrey SErtl T(2022)S4: Self-Supervised Learning of Spatiotemporal SimilarityIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2021.310141828:12(4713-4727)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1109/TVCG.2021.3101418
Tkachev GFrey SErtl T(2021)Local Prediction Models for Spatiotemporal Volume VisualizationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2019.296189327:7(3091-3108)Online publication date: 1-Jul-2021
https://dl.acm.org/doi/10.1109/TVCG.2019.2961893
Qu ZZhou YNguyen QCatchpoole D(2019)Using Visualization to Illustrate Machine Learning Models for Genomic DataProceedings of the Australasian Computer Science Week Multiconference10.1145/3290688.3290719(1-8)Online publication date: 29-Jan-2019
https://dl.acm.org/doi/10.1145/3290688.3290719
Giabbanelli PBaniukiewicz M(2019)Visual Analytics to Identify Temporal Patterns and Variability in Simulations from Cellular AutomataACM Transactions on Modeling and Computer Simulation10.1145/326574829:1(1-26)Online publication date: 24-Jan-2019
https://dl.acm.org/doi/10.1145/3265748
Soundararajan KSchultz T(2015)Learning Probabilistic Transfer FunctionsComputer Graphics Forum10.5555/2858877.285889034:3(111-120)Online publication date: 1-Jun-2015
https://dl.acm.org/doi/10.5555/2858877.2858890
Bakrawy LGhali NElla Hassanien A(2015)Intelligent Machine Learning in Image AuthenticationJournal of Signal Processing Systems10.1007/s11265-013-0817-478:2(223-237)Online publication date: 1-Feb-2015
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Starič ADemšar JZupan B(2015)Concurrent software architectures for exploratory data analysisWiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery10.1002/widm.11555:4(165-180)Online publication date: 1-Jul-2015
https://dl.acm.org/doi/10.1002/widm.1155
Yong LMingmin ZYunliang JHaiying Z(2012)Novel Applications of VRComputers and Graphics10.1016/j.cag.2012.01.00336:3(178-184)Online publication date: 1-May-2012
https://dl.acm.org/doi/10.1016/j.cag.2012.01.003
Nguyen QSimoff SHuang MDai GZhang KHuang MWang HYuan XTao LChen W(2010)Interactive visualization with user perspectiveProceedings of the 3rd International Symposium on Visual Information Communication10.1145/1865841.1865862(1-6)Online publication date: 28-Sep-2010
https://dl.acm.org/doi/10.1145/1865841.1865862

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