• 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