A Deep Learning Neural Network for Classifying Good and Bad Photos

摘要

Current state-of-the-art solutions that automate the assessment of photo aesthetic quality use deep learning neural networks. Most of these networks are either binary classifiers or regression models that predict the aesthetic quality of photos. In this paper, we developed a deep learning neural network that predicts the opinion score rating distribution of a photo's aesthetic quality. Our work focused on finding the best pre-processing method for improving the correlation between ground truth and predicted aesthetic rating distribution of photos in the AVA dataset. We investigated three ways of image resizing and two ways of extracting regions based on salience. We found that the best pre-processing method depended on the photos chosen for the training set.