Recognizing the Style of Artistic Painting via Information Entropy for Smart City Construction

Abstract

Digitalization is conducive to the protection and inheritance of culture and civilization. The artistic painting recognition is an essential part in digitalization and plays an important role in smart city construction. This paper proposes a novel framework to recognize Chinese painting style by using information entropy. First, the authors choose the ink painting, pyrography, mural, and splash ink painting as the known artistic styles. Then, this article uses the information entropy to represent the paintings. The information entropy includes color entropy, block entropy, and contour entropy. The color entropy is obtained by a weighted function of Channel A and B in the lab color space. The block entropy is the average information entropy of blocks which are a small part of the image. The contour entropy is obtained from the contour information which is obtained by contourlet transform. The information entropy is input into an oracle to determine the style. The oracle includes a one-class classifier and a classical classifier. The effectiveness is verified on the real painting set.