HIERARCHICAL FEATURES FUSION FOR IMAGE AESTHETICS ASSESSMENT

Image aesthetics assessment is an interesting yet challenging topic which can be applied on numerous scenarios such as high quality image retrieval or recommendation systems. We propose a hierarchical features fusion aesthetic assessment (HFFAA) model for this task. HFFAA is a two-stream convolutional neural network (CNN) which is composed of two branches with heterogeneous and complementary aesthetic perceptual abilities. HFFAA learns the mapping from deep image representation into their ground-truth aesthetic labels (good or bad) in an end-to-end fashion. Extensive experiments demonstrate that the proposed model achieves superior performance on two widely evaluated public benchmark databases, i.e., CUHKPQ and AVA. We also validate the rationality of the designs of HFFAA through a series of ablation experiments.