Abstract
Automatic image aesthetics assessment is a computer vision problem dealing with categorizing 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 adheres to the fundamental principles of photography such as balance, rhythm, harmony, contrast, unity, look, feel, tone, and texture. Due to its diverse applications in many areas, automatic image aesthetic assessment has gained significant research attention in recent years. This article presents a comparative study of different automatic image aesthetics assessment techniques from the year 2005 to 2021. A number of conventional hand-crafted as well as modern deep learning-based approaches are reviewed and analyzed for their performance on various publicly available datasets. Additionally, critical aspects of different features and models have also been discussed to analyze their performance and limitations in different situations. The comparative analysis reveals that deep learning based approaches excel hand-crafted based techniques in image aesthetic assessment.