Computer analysis for visual art style

Authors:
Yuqing Liu;Yuanyuan Pu;Dan Xu
Affiliations:
Yunnan University, Kunming;Yunnan University, Kunming;Yunnan University, Kunming
Venue:
SIGGRAPH Asia 2013 Technical Briefs
Year:
2013

Citing 4
Cited 0

From Abstract Painting to Information Visualization

IEEE Computer Graphics and Applications
Learning to Recognize the Art Style of Paintings Using Multi-cues

ICM '11 Proceedings of the 2011 International Conference of Information Technology, Computer Engineering and Management Sciences - Volume 01
Rhythmic Brushstrokes Distinguish van Gogh from His Contemporaries: Findings via Automated Brushstroke Extraction

IEEE Transactions on Pattern Analysis and Machine Intelligence
Toward Auvers period: evolution of van Gogh's style

Computational Aesthetics'10 Proceedings of the Sixth international conference on Computational Aesthetics in Graphics, Visualization and Imaging

Quantified Score

Hi-index	0.00

Visualization

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.