Feature Extraction and Analysis for Scientific Understanding of Visual Art

The authentication and classification of visual arts used to be depended on the judgment of the critic according to his experiences and knowledge about the artwork and artist. Recently, with the development of computer and image processing technology, the processing of digital visual artworks has become possible. More and more museums and libraries have their collections to be digitalized, which makes the study of problems haunted the art historians based on these digital images available. In this paper, the research of visual arts based on the computer and information of paintings, which can be called scientific understanding of visual arts, has been brought out. Some features, including multi-scale amplitude, non-stationarity of artworks, anisotropy of artworks and correlation of coefficients of the Curve let transform among scales are extracted based on the Curve let transform to evaluate the styles of different artists and some other respects of artworks. The relations between the styles of visual arts and these features are also stated, and the similarities of these styles are also qualified by comparing these features. It is apparent that each feature reflects different characteristics of the painting technique.