Predicting Scores of Various Aesthetic Attribute Sets by Learning from
  Overall Score Labels
Heng Huang Xin Jin Yaqi Liu Hao Lou Chaoen Xiao Shuai Cui Xinning Li Dongqing Zou
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.