NDLI: Computer analysis for visual art style

Content Provider	ACM Digital Library
Author	Xu, Dan Liu, Yuqing Pu, Yuanyuan
Abstract	In recent years, scholars pay more and more attention to the understanding and analysis of visual art style. This paper is based on Sparse Coding on visual art works, which brings out the trained basis function reflecting the style characteristics of a painting. Next, Gabor energy is extracted in Gabor domain from the trained basis function. Van Gogh's art works of different periods and Monet's art works are analyzed through normalized mutual information computing using trained basis function's Gabor energy to find the diversity of style. The experiment results show that Gabor energy can digitalize the intuitive feeling for basis function, and can distinguish the art styles of different works to a certain extent, and finally can provide reference for the criticism of art works.
Starting Page	1
Ending Page	4
Page Count	4
File Format	PDF
ISBN	9781450326292
DOI	10.1145/2542355.2542366
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-11-19
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Standard sparse coding Normalized mutual information Gabor filtering Visual art style
Content Type	Text
Resource Type	Article

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Computer analysis for visual art style