• DocumentCode
    595487
  • Title

    Towards automated classification of fine-art painting style: A comparative study

  • Author

    Arora, R.S. ; Elgammal, Ahmed

  • Author_Institution
    Dept. of Comput. Sci., Rutgers Univ., Piscataway, NJ, USA
  • fYear
    2012
  • fDate
    11-15 Nov. 2012
  • Firstpage
    3541
  • Lastpage
    3544
  • Abstract
    This paper presents a comparative study of different classification methodologies for the task of fine-art genre classification. 2-level comparative study is performed for this classification problem. 1st level reviews the performance of discriminative vs. generative models while 2nd level touches the features aspect of the paintings and compares semantic-level features vs low-level and intermediate level features present in the painting.
  • Keywords
    art; computer vision; image classification; 1st level; 2-level comparative study; automated classification; computer vision; fine-art genre classification; fine-art painting style; intermediate level features; low-level features; semantic-level features; Abstracts; Accuracy; Barium; Computer vision; Image color analysis; Painting; Support vector machines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Pattern Recognition (ICPR), 2012 21st International Conference on
  • Conference_Location
    Tsukuba
  • ISSN
    1051-4651
  • Print_ISBN
    978-1-4673-2216-4
  • Type

    conf

  • Filename
    6460929
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=595487