From Concepts to Judgments: Interpretable Image Aesthetic Assessment

TL;DR

This paper introduces an interpretable image aesthetic assessment framework that uses human-understandable concepts and a residual predictor, achieving competitive accuracy while providing transparent aesthetic judgments.

Contribution

It proposes a novel interpretable model for IAA based on aesthetic concepts and residual learning, enhancing understanding of prediction factors.

Findings

Achieves competitive predictive performance on photographic and artistic datasets.

Provides transparent, human-understandable aesthetic judgments.

Balances interpretability with accuracy in image aesthetic assessment.

Abstract

Image aesthetic assessment (IAA) aims to predict the aesthetic quality of images as perceived by humans. While recent IAA models achieve strong predictive performance, they offer little insight into the factors driving their predictions. Yet for users, understanding why an image is considered pleasing or not is as valuable as the score itself, motivating growing interest in interpretability within IAA. When humans evaluate aesthetics, they naturally rely on high-level cues to justify their judgments. Motivated by this observation, we propose an interpretable IAA framework grounded in human-understandable aesthetic concepts. We learn these concepts in an accessible manner, constructing a subspace that forms the foundation of an inherently interpretable model. To capture nuanced influences on aesthetic perception beyond explicit concepts, we introduce a simple yet effective residual predictor. Experiments on photographic and artistic datasets demonstrate that our method achieves competitive predictive performance while offering transparent, human-understandable aesthetic judgments.