Learning relative aesthetic quality with a pairwise approach

Abstract

Image aesthetic quality assessment is very useful in many multimedia applications. However, most existing researchers restrict quality assessment to a binary classification problem, which is to classify the aesthetic quality of images into “high” or “low” category. The strategy they applied is to learn the mapping from the aesthetic features to the absolute binary labels of images. The binary label description is restrictive and fails to capture the general relative relationship between images. We propose a pairwise-based ranking framework that takes image pairs as input to address this challenge. The main idea is to generate and select image pairs to utilize the relative ordering information between images rather than the absolute binary label information. We test our approach on two large scale and public datasets. The experimental results show our clear advantages over traditional binary classification-based approach.