Towards Artistic Image Aesthetics Assessment: a Large-scale Dataset and a New Method

• URL: http://arxiv.org/abs/2303.15166v1
• Date: Mon, 27 Mar 2023 12:59:15 GMT
• Title: Towards Artistic Image Aesthetics Assessment: a Large-scale Dataset and a New Method
• Authors: Ran Yi, Haoyuan Tian, Zhihao Gu, Yu-Kun Lai and Paul L. Rosin
• Abstract summary: We first introduce a large-scale AIAA dataset: Boldbrush Artistic Image dataset (BAID), which consists of 60,337 artistic images covering various art forms. We then propose a new method, SAAN, 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.
• Score: 64.40494830113286
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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: https://github.com/Dreemurr-T/BAID.git

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