Predicting beauty, liking, and aesthetic quality: A comparative analysis
  of image databases for visual aesthetics research
Ralf Bartho Katja Thoemmes Christoph Redies
In the fields of Experimental and Computational Aesthetics, numerous image
datasets have been created over the last two decades. In the present work, we
provide a comparative overview of twelve image datasets that include aesthetic
ratings (beauty, liking or aesthetic quality) and investigate the
reproducibility of results across different datasets. Specifically, we examine
how consistently the ratings can be predicted by using either (A) a set of 20
previously studied statistical image properties, or (B) the layers of a
convolutional neural network developed for object recognition. Our findings
reveal substantial variation in the predictability of aesthetic ratings across
the different datasets. However, consistent similarities were found for
datasets containing either photographs or paintings, suggesting different
relevant features in the aesthetic evaluation of these two image genres. To our
surprise, statistical image properties and the convolutional neural network
predict aesthetic ratings with similar accuracy, highlighting a significant
overlap in the image information captured by the two methods. Nevertheless, the
discrepancies between the datasets call into question the generalizability of
previous research findings on single datasets. Our study underscores the
importance of considering multiple datasets to improve the validity and
generalizability of research results in the fields of experimental and
computational aesthetics.