Image aesthetic quality assessment: A method based on deep convolutional capsule network

Abstract

Image aesthetics assessment (IAA) has become a hot research area in recent years due to its extensive application potential. However, existing IAA methods often overlook the importance of spatial information in evaluating image aesthetics. To address this limitation, this study proposes a novel method called the Deep Convolutional Capsule Network (DCCN), which integrates an improved Inception module with a capsule routing mechanism to enhance the representation of spatial features—an essential yet frequently underexplored aspect in aesthetic evaluation. This design enables the model to effectively extract both global and local aesthetic features while maintaining spatial relationships. To the best of our knowledge, this is the first attempt to apply capsule networks in the IAA domain. Experiments conducted on two benchmark datasets, CUHK-PQ and AVA, demonstrate the effectiveness of the proposed method. The DCCN achieves a classification accuracy of 94.79% on CUHK-PQ, and on AVA, it obtains a Pearson Linear Correlation Coefficient (PLCC) of 0.8408 and a Spearman Rank-Ordered Correlation Coefficient (SROCC) of 0.7394. While the DCCN shows promising results, it exhibits sensitivity to style variations and resolution changes and has relatively high inference complexity due to dynamic routing, which may affect deployment in real-time applications.