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Issue No.05 - September/October (2007 vol.27)
pp: 6-9
Kwan-Liu Ma , University of California at Davis
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.
INDEX TERMS
information visualization, intelligent systems, interface design, machine learning, scientific visualization
CITATION
Kwan-Liu Ma, "Machine Learning to Boost the Next Generation of Visualization Technology", IEEE Computer Graphics and Applications, vol.27, no. 5, pp. 6-9, September/October 2007, doi:10.1109/MCG.2007.129
REFERENCES
1. P.-N. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining, Addison-Wesley, 2005.
2. A. Hertzmann, "Machine Learning for Computer Graphics: A Manifesto and Tutorial," Proc. Pacific Graphics Conf., IEEE CS Press, 2003, pp. 22–36.
3. 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.
4. H. Pfister et al., "The Transfer Function Bake-Off," IEEE Computer Graphics and Applications, vol. 21, no. 3, 2001, pp. 16–23.
5. 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.
6. 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.
7. 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.
8. 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.
9. 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.
10. J. Kniss et al., "Statistically Quantitative Volume Visualization," Proc. Visualization Conf., IEEE CS Press, 2005, pp. 287–294.
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