Automatic assessment of online fashion shopping photo aesthetic quality

Photo aesthetic quality assessment is a challenging task. In this paper we propose a framework to automatically assess the aesthetic quality of online shopping photos. Novel image features that indicate photo aesthetic quality are introduced. We further investigate the relevance between our image features and photo aesthetic quality with the elastic net. A ranking of features in the order of their relevance to photo aesthetic quality is thus obtained. Moreover, we apply the wrapper feature selection methodology with the best-first searching algorithm to establish an optimal feature subset that yields best prediction accuracy. With a photo database, we adopt the support vector regression (SVR) technique to train an aesthetic quality predictor using the selected optimal feature subset. Promising prediction accuracy is obtained with cross-validation.