梵高绘画风格特点的数字化分析

Digital Analysis for Van Gogh's Painting

利用稀疏编码算法提取的图像特征可用于视觉艺术作品的风格分析,但往往并不对训练所得的基函数做深度的数据挖掘。通过对视觉艺术作品的稀疏变换,获得能反映风格图像本质特征的训练基,对训练基进行空间特征量统计及高阶特征量统计。利用训练基的Gabor变换能量,对梵高不同时期风格作品进行归一化互信息计算,从而对梵高不同时期艺术作品差异特点做出分析。仿真结果表明,对基函数的深度挖掘,能够将人们对基函数的直观感受进行数字量化,在一定程度上对作品风格做出区分,并可为画作评论提供一定参考。

Usually, Sparse Coding can be used to extract image characteristics for analyzing the style of visual art works, while researchers always don't do deep data mining on the trained basis function. The basic functions that could reflect the interior characteristics of a stylish image were trained based on Sparse Coding on art works. The spatial and higher-order characteristic statistics were figured out. Van Gogh's art works of different periods were analyzed through normalized mutual information computing using trained basis function's Gabor transform power, in order to find the diversity of style. The simulation results show that data mining on basis function 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.