Image Aesthetics Assessment Based on Multi-stream CNN Architecture and Saliency Features

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.

Automated summary

This paper investigates how higher-level perceptual information based on visual attention can be utilized for aesthetic assessment of images. By extracting global features and saliency features, the impact of visually dominant subjects on aesthetic interest is explored. Experimental results confirm the effectiveness of this approach.