Understanding The Aesthetic Styles of Social Images

Abstract

Aesthetic perception is nearly the most direct impact people could receive from images. Recent research on image understanding is mainly focused on image analysis, recognition and classification, regardless of the aesthetic meanings embedded in images. In this paper, we systematically study the problem of understanding the aesthetic styles of social images. First, we build a two-dimensional Image Aesthetic Space (IAS) to describe image aesthetic styles quantitatively and universally. Then, we propose a Bimodal Deep Autoen-coder with Cross Edges (BDA-CE) to deeply fuse the social image related features (i.e. images' visual features, tags' textual features). Connecting BDA-CE with a regression model, we are able to map the features to the IAS. The experimental results on the benchmark dataset we build with 120 thousand Flickr images show that our model outperforms (+5.5% in terms of MSE) alternative baselines. Furthermore, we conduct an interesting case study to demonstrate the advantages of our methods.