Computer analysis for visual art style

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.