Predicting beauty, liking, and aesthetic quality: A comparative analysis of image databases for visual aesthetics research

TL;DR

This study compares twelve image datasets with aesthetic ratings, analyzing how consistently aesthetic qualities can be predicted using statistical image properties and neural network features, revealing genre-specific features and dataset variability.

Contribution

It provides a comprehensive comparison of multiple aesthetic datasets and evaluates the predictive power of statistical and neural network features across them.

Findings

Predictability of aesthetic ratings varies across datasets.

Photographs and paintings show different relevant features.

Statistical properties and neural network features predict ratings with similar accuracy.

Abstract

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.