ABSTRACT In this paper, we explore how higher-level perceptual information based on visual attention can be used for aesthetic assessment of images. We assume that visually dominant subjects in a photograph influence stronger aesthetic interest. In other words, visual attention may be a key to predicting photographic aesthetics. Our proposed aesthetic assessment method, which is based on multi-stream and multi-task convolutional neural networks (CNNs), extracts global features and saliency features from an input image. These provide higher-level visual information such as the quality of the photo subject and the subject–background relationship. Results from our experiments support the effectiveness of our approach.

中文翻译：

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基于多流CNN架构和显着特征的图像美学评估

摘要在本文中，我们探讨了如何将基于视觉注意的更高级别的感知信息用于图像的审美评估。我们假设照片中视觉主导的主题会影响更强的审美兴趣。换句话说，视觉注意力可能是预测摄影美学的关键。我们提出的美学评估方法基于多流和多任务卷积神经网络 (CNN)，从输入图像中提取全局特征和显着特征。这些提供了更高级别的视觉信息，例如照片主题的质量和主题-背景关系。我们的实验结果支持我们方法的有效性。