Image Aesthetic Assessment: An experimental survey

By Yubin Deng, Chen Change Loy, Xiaoou Tang. IEEE Signal Processing Magazine, 2016

303 citations recorded by the source corpus.

This article reviews recent computer vision techniques used in the assessment of image aesthetic quality. Image aesthetic assessment aims at computationally distinguishing high-quality from low-quality photos based on photographic rules, typically in the form of binary classification or quality scoring. A variety of approaches has been proposed in the literature to try to solve this challenging problem. In this article, we summarize these approaches based on visual feature types (hand-crafted features and deep features) and evaluation criteria (data set characteristics and evaluation metrics). The main contributions and novelties of the reviewed approaches are highlighted and discussed. In addition, following the emergence of deep-learning techniques, we systematically evaluate recent deep-learning settings that are useful for developing a robust deep model for aesthetic scoring.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/5743198e0448d6e0cd33acbf7b244a76366fb2a9

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Citations in the corpus

A Deep Network Solution for Attention and Aesthetics Aware Photo Cropping (2019, methodology): “Many methods have been proposed for this topic, as surveyed in [43].”
Neural Style Transfer: A Review (2017, background): “ the problem of standard aesthetic criteria is an extensive problem of Photographic Image Aesthetic Assessment, and one could get inspirations from related researches in this area. Here, we recommend [18] for an overview of Photographic Image Aesthetic Assessment. 17 9. Discussions and Conclusions O”