Inkthetics: A Comprehensive Computational Model for Aesthetic Evaluation of Chinese Ink Paintings

Abstract

Assessing the aesthetic appeal of artworks has become an active research direction recently. However, previous works mainly focus on photographs and oil paintings, there have been few attempts in predicting aesthetics of Chinese ink paintings, due to their significant differences in visual features, semantic features, and aesthetic principles. Aiming at this problem, we propose a comprehensive framework, named Inkthetics, to quantify aesthetics of Chinese ink paintings based on deep learning. Firstly, an aesthetic assessment dataset is built for Chinese ink painting images. Secondly, a deep multi-view parallel convolutional neural network (DMVCNN) is designed by extracting global attribute images and multi-patches as inputs to jointly learn aesthetic features. Finally, we build a comprehensive aesthetic evaluation model by fusing the deeply-learned features with handcrafted features that rely on art expert knowledge. Experimental results show that our proposed deep network significantly outperforms existing methods on the dataset, and our proposed model can predict human aesthetic judgment with Pearson highly significant correlation of 0.843, which indicates an improvement up to 5.7% than the DMVCNN model when the handcrafted features are fused with activation from DMVCNN. Our work not only provides a deep-learning-based reference framework for computational aesthetic evaluation of Chinese paintings, but also explores to what extent can handcrafted features aid learning-based features in predicting human aesthetic perceptions.