Integrating Subjective and Objective Features for Image Aesthetics Assessment

Image aesthetics assessment is commonly treated as a classification or regression task and its performance bottleneck mainly depends on the effective utilization of aesthetic features. Traditional methods tend to use only subjective or objective features. Only one type of feature overlooks the inherent unity of these features in aesthetic characteristics. To comprehensively utilize image aesthetic characteristics, we propose an innovative image aesthetics assessment method that integrates subjective and objective features. Specifically, the proposed method comprises two modules: feature extraction module and feature fusion module. In the feature extraction module, a dual-branch neural network is proposed to extract subjective and objective aesthetic features of the image. In the feature fusion module, a feature fusion module based on a deep feedforward neural network is proposed to combine these two types of features and generate the final image aesthetic score. The experimental results indicate that our method gets a high performance in aesthetics assessment.