  Automatic image aesthetics assessment is a computer vision problem that deals
with the categorization of images into different aesthetic levels. The
categorization is usually done by analyzing an input image and computing some
measure of the degree to which the image adhere to the key principles of
photography (balance, rhythm, harmony, contrast, unity, look, feel, tone and
texture). Owing to its diverse applications in many areas, automatic image
aesthetic assessment has gained significant research attention in recent years.
This paper presents a literature review of the recent techniques of automatic
image aesthetics assessment. A large number of traditional hand crafted and
deep learning based approaches are reviewed. Key problem aspects are discussed
such as why some features or models perform better than others and what are the
limitations. A comparison of the quantitative results of different methods is
also provided at the end.
