DocumentCode
1118978
Title
Machine Learning to Boost the Next Generation of Visualization Technology
Author
Ma, Kwan-Liu
Author_Institution
California Univ., Davis
Volume
27
Issue
5
fYear
2007
Firstpage
6
Lastpage
9
Abstract
Visualization has become an indispensable tool in many areas of science and engineering. In particular, the advances made in the field of visualization over the past 20 years have turned visualization from a presentation tool to a discovery tool. Machine learning has received great success in both data mining and computer graphics; surprisingly, the study of systematic ways to employ machine learning in making visualization is meager. Like human learning, we can make a computer program learn from previous input data to optimize its performance on processing new data. In the context of visualization, the use of machine learning can potentially free us from manually sifting through all the data. This paper describes intelligent visualization designs for three different applications: (1) volume classification and visualization, (2) 4D flow feature extraction and tracking, (3) network scan characterization.
Keywords
data visualisation; learning (artificial intelligence); rendering (computer graphics); 4D flow feature extraction; 4D flow feature tracking; computer graphics; intelligent visualization designs; machine learning; network scan characterization; volume classification; volume rendering; volume visualization; Biological neural networks; Data mining; Data visualization; Feature extraction; Intelligent systems; Machine learning; Painting; Paints; Transfer functions; User interfaces; information visualization; intelligent systems; interface design; machine learning; scientific visualization;
fLanguage
English
Journal_Title
Computer Graphics and Applications, IEEE
Publisher
ieee
ISSN
0272-1716
Type
jour
DOI
10.1109/MCG.2007.129
Filename
4302576
Link To Document