Avoiding the Inherent Limitations in Datasets Used for Measuring Aesthetics When Using a Machine Learning Approach

Abstract

[Abstract]: An important topic in evolutionary art is the development of systems that can mimic the aesthetics decisions made by human begins,e.g., fitness evaluations made by humans using interactive evolution in generative art. This paper focuses on the analysis of severaldatasets used for aesthetic prediction based on ratings from photography websites and psychological experiments. Since thesedatasets present problems, we proposed a new dataset that is a subset of DPChallenge.com. Subsequently, three different evaluationmethods were considered, one derived from the ratings available at DPChallenge.com and two obtained under experimentalconditions related to the aesthetics and quality of images. We observed different criteria in the DPChallenge.com ratings, whichhad more to do with the photographic quality than with the aesthetic value. Finally, we explored learning systems other than state-of-the-art ones, in order to predict these three values. The obtained results were similar to those using state-of-the-art procedures.