• DocumentCode
    1587071
  • Title

    Identification of artistic styles using a local statistical metric

  • Author

    Wayner, Peter

  • Author_Institution
    Dept. of Comput. Sci., Cornell Univ., Ithaca, NY, USA
  • fYear
    1991
  • Firstpage
    110
  • Lastpage
    113
  • Abstract
    An algorithm for identifying the artist who created a picture is described. The algorithm relies upon computing the distribution of long and short lines in the image and comparing this distribution. The algorithm is one example of algorithms which can be designed to answer questions about global characteristics. This particular example computes the averages against a precomputed set from model samples. The distribution of line lengths is a statistical characterization which can be used to distinguish between artists. The current implementation is limited to black-and-white binary images. The results of testing the implementation on the daily comics is presented. Some of the related work in both computer science and art history which provides a conceptual background for the algorithm is also discussed
  • Keywords
    art; computer graphics; computerised pattern recognition; art history; artistic styles; black-and-white binary images; computer science; conceptual background; global characteristics; line lengths; local statistical metric; model samples; precomputed set; short lines; statistical characterization; Art; Artificial intelligence; Books; Computer science; Distributed computing; History; Humans; Machine vision; Partitioning algorithms; Testing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Artificial Intelligence Applications, 1991. Proceedings., Seventh IEEE Conference on
  • Conference_Location
    Miami Beach, FL
  • Print_ISBN
    0-8186-2135-4
  • Type

    conf

  • DOI
    10.1109/CAIA.1991.120854
  • Filename
    120854