• DocumentCode
    2094377
  • Title

    Feature Extraction and Analysis for Scientific Understanding of Visual Art

  • Author

    Zhang Yi ; Pu Yuanyuan ; Huang Yaqun ; Xu Dan ; Qian Wenhua

  • Author_Institution
    Sch. of Inf. Sci. & Eng., Yunnan Univ., Kunming, China
  • fYear
    2013
  • fDate
    16-18 Nov. 2013
  • Firstpage
    411
  • Lastpage
    412
  • Abstract
    In this paper, the research of visual art based on the computer and information of paintings, which can be called scientific understanding of visual art, has been brought out. Four features, multi-scale amplitude, non-stationarity of artworks, anisotropy of artworks and correlation of coefficients of the curve let transform between scales, are extracted based on the curve let transform to evaluate the style of different artists. The relations between the style of visual art and these features are also stated, and the similarities of these styles are also qualified by comparing these features. It is apparent that each feature reflects different characteristics of the different school paintings.
  • Keywords
    art; curvelet transforms; feature extraction; artwork anisotropy; artwork nonstationarity; curvelet transform; feature extraction; multiscale amplitude; school paintings; scientific understanding; visual art; Art; Correlation; Educational institutions; Feature extraction; Painting; Transforms; Visualization; Computational Aesthetics; Curvelet transform; Scientific understanding; Styles; Visual art;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer-Aided Design and Computer Graphics (CAD/Graphics), 2013 International Conference on
  • Conference_Location
    Guangzhou
  • Type

    conf

  • DOI
    10.1109/CADGraphics.2013.72
  • Filename
    6815036