Artistic Image Aesthetics Assessment Assisted by Photographic Visual Attributes

Most data-driven deep learning-based Artistic Image Aesthetics Assessment (AIAA) methods cannot effectively extract visual attributes from art images since the existing artistic image datasets don’t provide any information about visual attributes. The lack of visual attributes reduces the interpretability of AIAA methods and limits their performance. To address these problems, a novel artistic image aesthetics assessment assisted by photographic visual attributes is proposed. The proposed method consists of a feature extraction module and a joint prediction module. The feature extraction module pre-trained on a photographic dataset and an artistic image dataset can learn the information of photographic attributes and the generic artistic aesthetic information. The joint prediction module uses a non-local self-attention block to fuse the photographic visual attribute features with general artistic aesthetic features. The fused features are fed into an FC layer for calculating the artistic image aesthetic score. Experimental results indicate that our proposed method outperforms some state-of-the-art AIAA methods.