Image Aesthetic Assessment: An Experimental Survey

TL;DR

This survey reviews recent computer vision methods for assessing image aesthetic quality, focusing on visual features, evaluation criteria, and deep learning techniques, providing a comprehensive reference for future research in the field.

Contribution

It systematically categorizes approaches based on features and evaluation metrics, and evaluates deep learning models for aesthetic scoring, highlighting main contributions and future directions.

Findings

Deep learning models improve aesthetic assessment accuracy.

Simple baselines can be competitive with state-of-the-art methods.

Computational approaches can manipulate image aesthetics.

Abstract

This survey aims at reviewing recent computer vision techniques used in the assessment of image aesthetic quality. Image aesthetic assessment aims at computationally distinguishing high-quality photos from low-quality ones based on photographic rules, typically in the form of binary classification or quality scoring. A variety of approaches has been proposed in the literature trying to solve this challenging problem. In this survey, we present a systematic listing of the reviewed approaches based on visual feature types (hand-crafted features and deep features) and evaluation criteria (dataset characteristics and evaluation metrics). 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. Experiments are conducted using simple yet solid baselines that are competitive with the current state-of-the-arts. Moreover, we discuss the possibility of manipulating the aesthetics of images through computational approaches. We hope that our survey could serve as a comprehensive reference source for future research on the study of image aesthetic assessment.