Psychology Inspired Model for Hierarchical Image Aesthetic Attribute Prediction

Abstract

Deep neural network has proved its effectiveness in image aesthetic quality assessment (IAQA), but still lacks reasonable interpretability. Aesthetic attributes provide rich intermediate-level information for understanding the underlying principles of image aesthetics, but has not been fully investigated. Psychological studies have shown that aesthetic experience involves hierarchical stages, i.e., human process image aesthetics following a staged information processing mechanism. Motivated by this, this paper presents a Hierarchical Image Aesthetic Attribute (HIAA) prediction model, aiming to imitate the staged mechanism of human aesthetic experience. Image aesthetic attributes are first divided into several hierarchical groups. Then, hierarchical features are extracted from the cascaded layers of the deep neural network to predict the aesthetic attributes in a group-wise manner. The overall image aesthetic score is also predicted by aggregating the hierarchical features. Experimental results demonstrate that the proposed HIAA model outperforms the state-of-the-arts in terms of both aesthetic attribute prediction and aesthetic score regression.