  Personalized image aesthetics assessment (PIAA) is challenging due to its
highly subjective nature. People's aesthetic tastes depend on diversified
factors, including image characteristics and subject characters. The existing
PIAA databases are limited in terms of annotation diversity, especially the
subject aspect, which can no longer meet the increasing demands of PIAA
research. To solve the dilemma, we conduct so far, the most comprehensive
subjective study of personalized image aesthetics and introduce a new
Personalized image Aesthetics database with Rich Attributes (PARA), which
consists of 31,220 images with annotations by 438 subjects. PARA features
wealthy annotations, including 9 image-oriented objective attributes and 4
human-oriented subjective attributes. In addition, desensitized subject
information, such as personality traits, is also provided to support study of
PIAA and user portraits. A comprehensive analysis of the annotation data is
provided and statistic study indicates that the aesthetic preferences can be
mirrored by proposed subjective attributes. We also propose a conditional PIAA
model by utilizing subject information as conditional prior. Experimental
results indicate that the conditional PIAA model can outperform the control
group, which is also the first attempt to demonstrate how image aesthetics and
subject characters interact to produce the intricate personalized tastes on
image aesthetics. We believe the database and the associated analysis would be
useful for conducting next-generation PIAA study. The project page of PARA can
be found at: https://cv-datasets.institutecv.com/#/data-sets.
