Semantic Attribute Guided Image Aesthetics Assessment

Abstract

Image aesthetics assessment (IAA) measures the perceived beauty of images using a computational approach. People usually assess the aesthetics of an image according to semantic attributes, e.g., lighting, color, object emphasis, etc. However, the state-of-the-art IAA approaches usually follow the data-driven framework without considering the rich attributes contained in images. With this motivation, this paper presents a new semantic attribute guided IAA model, where the attention maps of semantic attributes are employed to enhance the representation ability of general aesthetic features for more effective aesthetics assessment. Specifically, we first design an attribute attention generation network to obtain the attention maps for different semantic attributes, which are utilized to weight the general aesthetic features, producing the semantic attribute-enhanced feature representations. Then, the Graph Convolutional Network (GCN) is employed to further investigate the inherent relationship among the enhanced aesthetic features, producing the final image aesthetics prediction. Extensive experiments and comparisons on three public IAA databases demonstrate the effectiveness of the proposed method.