BackFlip: The Impact of Local and Global Data Augmentations on Artistic Image Aesthetic Assessment
This work addresses the challenge of aesthetic evaluation for art images, which is incremental as it adapts existing augmentation methods to a specific domain.
The paper tackled the problem of assessing aesthetic quality in artistic images by exploring local and global data augmentation techniques, finding that their introduced local augmentation, BackFlip, outperformed global ones in most cases on three datasets and four neural network architectures.
Assessing the aesthetic quality of artistic images presents unique challenges due to the subjective nature of aesthetics and the complex visual characteristics inherent to artworks. Basic data augmentation techniques commonly applied to natural images in computer vision may not be suitable for art images in aesthetic evaluation tasks, as they can change the composition of the art images. In this paper, we explore the impact of local and global data augmentation techniques on artistic image aesthetic assessment (IAA). We introduce BackFlip, a local data augmentation technique designed specifically for artistic IAA. We evaluate the performance of BackFlip across three artistic image datasets and four neural network architectures, comparing it with the commonly used data augmentation techniques. Then, we analyze the effects of components within the BackFlip pipeline through an ablation study. Our findings demonstrate that local augmentations, such as BackFlip, tend to outperform global augmentations on artistic IAA in most cases, probably because they do not perturb the composition of the art images. These results emphasize the importance of considering both local and global augmentations in future computational aesthetics research.