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.

翻译：评估艺术图像的美学质量面临独特挑战，这源于美学的主观性以及艺术作品固有的复杂视觉特征。计算机视觉中常用于自然图像的基础数据增强技术可能不适用于美学评估任务中的艺术图像，因为它们可能改变艺术图像的构图。本文探讨了局部与全局数据增强技术对艺术图像美学评估的影响。我们提出了BackFlip——一种专为艺术图像美学评估设计的局部数据增强技术。我们在三个艺术图像数据集和四种神经网络架构上评估BackFlip的性能，并将其与常用数据增强技术进行比较。随后通过消融实验分析了BackFlip流程中各组件的影响。研究结果表明，在大多数情况下，如BackFlip这类局部增强方法在艺术图像美学评估中往往优于全局增强方法，这可能是因为它们不会破坏艺术图像的构图。这些发现强调了在未来计算美学研究中综合考虑局部与全局数据增强的重要性。