Challenges of finding aesthetically pleasing images

We present an analysis of existing methods to automatic classification of photos according to aesthetics. We review different components of the classification process: existing evaluation datasets, their properties, most commonly-used image features, qualitative and quantitative, and classification results where comparable. We argue there are methodology gaps in the existing approaches to evaluating the classification results. We introduce the results of our experiments with Random Forest classification applied to image aesthetics classification and compare them to AdaBoost and SVM approaches.