Aesthetic image analysis is the study and assessment of the aesthetic
properties of images. Current computational approaches to aesthetic image
analysis either provide accurate or interpretable results. To obtain both
accuracy and interpretability by humans, we advocate the use of learned and
nameable visual attributes as mid-level features. For this purpose, we propose
to discover and learn the visual appearance of attributes automatically, using
a recently introduced database, called AVA, which contains more than 250,000
images together with their aesthetic scores and textual comments given by
photography enthusiasts. We provide a detailed analysis of these annotations as
well as the context in which they were given. We then describe how these three
key components of AVA - images, scores, and comments - can be effectively
leveraged to learn visual attributes. Lastly, we show that these learned
attributes can be successfully used in three applications: aesthetic quality
prediction, image tagging and retrieval.

本文提出了使用 AVA 数据库，自动发现和学习视觉属性来实现美学质量预测、图像标记和检索的目的。