Modeling image aesthetics through aesthetics-related attributes

This is the authors' abstract. We don't add key points for this paper.

Automatic assessment of image aesthetics is a challenging task for the computer vision community that has a wide range of applications. The most promising state-of-the-art approaches are based on deep learning methods that jointly predict aesthetics-related attributes and aesthetics score. In this article, we propose a method that learns the aesthetics score on the basis of the prediction of aesthetics-related attributes. To this end, we extract a multi-level spatially pooled (MLSP) features set from a pretrained ImageNet network and then these features are used to train a Multi Layer Perceptron (MLP) to predict image aesthetics-related attributes. A Support Vector Regression machine (SVR) is finally used to estimate the image aesthetics score starting from the aesthetics-related attributes. Experimental results on the ”Aesthetics with Attributes Database” (AADB) demonstrate the effectiveness of our approach that outperforms the state of the art of about 5.5% in terms of Spearman’s Rankorder Correlation Coefficient (SROCC).