A Multi-Patch Aggregated Aesthetic Rating System Based on Eyefixation

Due to individuals have their own aesthetics so evaluations of the same photograph may differ, it is essential to develop an assessment system that approximates the aesthetics of general public. This paper utilize the large-scale aesthetic Aesthetic Visual Analysis dataset (AVA) to help establish an aesthetic assessment system. In addition to a machine perspective, we further propose a human-like eye fixation method that enables machines to learn from human perspective when analyzing aesthetics from data, which can be used as a reference for future machine learning systems to learn abstract features from a human perspective. Our system selects the areas that attract the most attention of viewers as patches and uses the overall image as the overall image layout, which are then analyzed by the multi-patch aggregated aesthetic model. The performance improvements are validated by linear correlation coefficient and mean square error.