Self-Adaptive Computational Aesthetic Evaluation of Chinese Ink Paintings Based on Deep Learning

Abstract

<p indent=0mm>Computational aesthetic evaluation of artworks has become an active research direction in recent years. However, current works mainly focus on oil paintings and photographs, there have been few attempts in quantitative aesthetic evaluation of Chinese ink paintings. Chinese ink painting uses ink blended with water and a variation of brushwork to depict picture, which differs significantly from photographs and oil paintings in visual features, semantic features, and aesthetic principles. Aiming at this problem, we propose a framework of self-adaptive computational aesthetic evaluation of Chinese ink paintings based on deep learning technique. Firstly, we build an aesthetic evaluation standard dataset for ink painting images. Secondly, according to aesthetic principles of Chinese ink paintings, we design a multi-view parallel deep neural network by taking global images and local patches as multi-column inputs to extract deep aesthetic features. Finally, we build a self-adaptive deep aesthetic model of Chinese ink paintings based on subject query mechanism. Experimental results show that, compared with the basic VGG16 architecture, our multi-view network that contains six paralleled subject convolution groups has higher aesthetic evaluation performance, the deep aesthetic features outperform the traditional hand-crafted features, and our proposed self-adaptive model can predict human aesthetic decision with highly significant Pearson correlation of 0.823, with mean squared error of 0.161. Moreover, interference experiments show that our network is sensitive to painting factors including composition, ink color, and texture. Our work not only offers a deeply-learned-based reference framework for quantitative aesthetic evaluation of Chinese paintings, but also reveals the relationship between human aesthetic perceptions and deeply-learned features extracted from Chinese ink paintings.