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
Link To Document