Predicting Scores of Various Aesthetic Attribute Sets by Learning from Overall Score Labels

• URL: http://arxiv.org/abs/2312.03222v1
• Date: Wed, 6 Dec 2023 01:41:49 GMT
• Title: Predicting Scores of Various Aesthetic Attribute Sets by Learning from Overall Score Labels
• Authors: Heng Huang, Xin Jin, Yaqi Liu, Hao Lou, Chaoen Xiao, Shuai Cui, Xinning Li, Dongqing Zou
• Abstract summary: In this paper, we propose to replace image attribute labels with feature extractors. We use networks from different tasks to provide attribute features to our F2S model. Our method makes it feasible to learn meaningful attribute scores for various aesthetic attribute sets in different types of images with only overall aesthetic scores.
• Score: 54.63611854474985
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Now many mobile phones embed deep-learning models for evaluation or guidance on photography. These models cannot provide detailed results like human pose scores or scene color scores because of the rare of corresponding aesthetic attribute data. However, the annotation of image aesthetic attribute scores requires experienced artists and professional photographers, which hinders the collection of large-scale fully-annotated datasets. In this paper, we propose to replace image attribute labels with feature extractors. First, a novel aesthetic attribute evaluation framework based on attribute features is proposed to predict attribute scores and overall scores. We call it the F2S (attribute features to attribute scores) model. We use networks from different tasks to provide attribute features to our F2S models. Then, we define an aesthetic attribute contribution to describe the role of aesthetic attributes throughout an image and use it with the attribute scores and the overall scores to train our F2S model. Sufficient experiments on publicly available datasets demonstrate that our F2S model achieves comparable performance with those trained on the datasets with fully-annotated aesthetic attribute score labels. Our method makes it feasible to learn meaningful attribute scores for various aesthetic attribute sets in different types of images with only overall aesthetic scores.

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