RAPID: Rating Pictorial Aesthetics using Deep Learning
Effective visual features are essential for computational aes-thetic quality rating systems. Existing methods used ma-chine learning and statistical modeling techniques on hand-crafted features or generic image descriptors. A recently-published large-scale dataset, the AVA dataset, has further empowered machine learning based approaches. We present the RAPID (RAting PIctorial aesthetics using Deep learn-ing) system, which adopts a novel deep neural network ap-proach to enable automatic feature learning. The central idea is to incorporate heterogeneous inputs generated from the image, which include a global view and a local view, and to unify the feature learning and classifier training using a double-column deep convolutional neural network. In addi-tion, we utilize the style attributes of images to help improve the aesthetic quality categorization accuracy. Experimental results show that our approach significantly outperforms the state of the art on the AVA dataset.