Predicting image aesthetics with deep learning

Abstract

In this paper we investigate the use of a deep Convolutional Neural Network (CNN) to predict image aesthetics. To this end we finetune a canonical CNN architecture, originally trained to classify objects and scenes, by casting the image aesthetic prediction as a regression problem. We also investigate whether image aesthetic is a global or local attribute, and the role played by bottom-up and top-down salient regions to the prediction of the global image aesthetic. Experimental results on the canonical Aesthetic Visual Analysis (AVA) dataset show the robustness of the solution proposed, which outperforms the best solution in the state of the art by almost 17% in terms of Mean Residual Sum of Squares Error (MRSSE).