This survey aims at reviewing recent techniques used in the assessment of image aesthetic quality. The assessment of image aesthetic quality is the process of computationally distinguishing high-quality photos from low-quality ones based on photographic rules or artistic perceptions. A variety of approaches have 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 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 relation between image aesthetic assessment and automatic image cropping. We hope that this survey could serve as a comprehensive reference source for future research on the study of image aesthetic assessment.
['Yubin Deng', 'Chen Change Loy', 'Xiaoou Tang']
Image Aesthetic Assessment: An Experimental Survey