Interpretable Aesthetic Analysis Model for Intelligent Photography Guidance Systems

TL;DR

This paper presents an interpretable aesthetic evaluation model using hyper-networks and attention mechanisms, enabling better understanding of visual elements influencing aesthetic scores in photography guidance systems.

Contribution

It introduces a novel hyper-network based approach with attention mechanisms for interpretable aesthetic assessment in photography guidance.

Findings

Model achieves high interpretability and accuracy

User studies confirm improved understanding of aesthetic judgments

System effectively guides users in photography composition

Abstract

An aesthetics evaluation model is at the heart of predicting users' aesthetic experience and developing user interfaces with higher quality. However, previous methods on aesthetic evaluation largely ignore the interpretability of the model and are consequently not suitable for many human-computer interaction tasks. We solve this problem by using a hyper-network to learn the overall aesthetic rating as a combination of individual aesthetic attribute scores. We further introduce a specially designed attentional mechanism in attribute score estimators to enable the users to know exactly which parts/elements of visual inputs lead to the estimated score. We demonstrate our idea by designing an intelligent photography guidance system. Computational results and user studies demonstrate the interpretability and effectiveness of our method.