DNN-based Photography Rule Prediction using Photo Tags

Instagram and Flickr are just two examples of photo-sharing platforms which are currently used to upload thousands of images on a daily basis. One important aspect in such social media contexts is to know whether an image is of high appeal or not. In particular, to understand the composition of a photo and to improve reading flow, several photo rules have been established. In this paper, we focus on eight selected photo rules. To automatically predict whether an image follows one of these rules or not, we train 13 deep neural networks in a transfer-learning setup and compare their prediction performance. As a dataset, we use photos downloaded from Flickr with specifically selected image tags, which reflect the eight photo rules. There-fore, our dataset does not need additional human annotations. ResNet50 has the best prediction performance, however, there are images that follow several rules, which must be addressed in follow-up work. The code and the data (image URLs) are made publicly available for reproducibility.