A Comprehensive Survey on Computational Aesthetic Evaluation of Visual Art Images: Metrics and Challenges

Abstract

Computational image aesthetic evaluation is a computable human aesthetic perception and judgment realized by machines, which has a significant impact on a variety of applications such as image advanced search and promotional exhibition of painting arts. Various approaches have been proposed in copious literature trying to solve this challenging problem. However, there have been few attempts in reviewing works from different types of visual arts, due to their significant differences in visual features and aesthetic principles. In this survey, we present a comprehensive listing of the reviewed works on aesthetic assessment of photographs and paintings, mainly highlighting the contributions and innovations of the existing approaches. We firstly introduce aesthetic assessment benchmark datasets in different categories. Then, conventional aesthetic evaluation approaches based on handcrafted features are reviewed. Besides, we systematically evaluate recent deep learning techniques that are useful for developing robust models for aesthetic prediction tasks in scoring, distribution, attribute, and description. Moreover, the possibility of aesthetic-aware color enhancement, recomposition of photo images, and automatic generation of aesthetic-guided art paintings through computational approaches are summarized. Finally, challenges and potential future directions for this field are discussed. We hope that our survey could serve as a comprehensive reference source for future research on computational aesthetics in visual media.

Keywords

• Computational aesthetic evaluation
• visual art image
• handcrafted features
• deep neural networks
• aesthetic enhancement