Image aesthetics assessment (IAA) is a challenging task due to its highly
subjective nature. Most of the current studies rely on large-scale datasets
(e.g., AVA and AADB) to learn a general model for all kinds of photography
images. However, little light has been shed on measuring the aesthetic quality
of artistic images, and the existing datasets only contain relatively few
artworks. Such a defect is a great obstacle to the aesthetic assessment of
artistic images. To fill the gap in the field of artistic image aesthetics
assessment (AIAA), we first introduce a large-scale AIAA dataset: Boldbrush
Artistic Image Dataset (BAID), which consists of 60,337 artistic images
covering various art forms, with more than 360,000 votes from online users. We
then propose a new method, SAAN (Style-specific Art Assessment Network), which
can effectively extract and utilize style-specific and generic aesthetic
information to evaluate artistic images. Experiments demonstrate that our
proposed approach outperforms existing IAA methods on the proposed BAID dataset
according to quantitative comparisons. We believe the proposed dataset and
method can serve as a foundation for future AIAA works and inspire more
research in this field. Dataset and code are available at:
this https URL

本研究提出了一个大规模的 Boldbrush 艺术图像数据集，并采用 SAAN 方法从中提取和利用风格专属和通用美学信息来评估艺术图像，结果表明该方法在数据集上表现优异，这为未来的艺术图像美学评估研究提供了基础。