Beauty in the Eye of Machine

Using Automated Measures of Aesthetic Beauty to Improve GAN Output of Satellite Images

Bachelor thesis (2022)

Authors

J.M. Catlett Electrical Engineering, Mathematics and Computer Science

Contributors

J.D. Lomas Design Aesthetics - IDE (supervisor 1)
W.L.A. van der Maden Design Aesthetics - IDE (supervisor 1)
Ujwal Gadiraju Web Information Systems - EEMCS (supervisor 1)
G.M. Allen Web Information Systems - EEMCS (supervisor 1)
D.H.J. Epema Data-Intensive Systems - EEMCS (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2022 Joseph Catlett
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Published Date

23-06-2022

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

This paper aims to evaluate which automated measures of aesthetic beauty are the best predictors for human ratings of aesthetics and proposes that typicality and novelty may increase the correlation between the two. To study the correlation between these metrics, a literature study was performed to find a select amount of potentially good predictors, a pipeline was created to extract these values from each image within our dataset, a survey was conducted to vote for which images were considered most aesthetic, and finally regression analysis was performed to see which metrics offered highest correlation with the human rating data. From this we could see there were indeed a number of automated metrics that consistently scored high as predictors for the human aesthetic ratings and there was a slight improvement in the fit of the prediction model upon including novelty as a feature. However, at this moment, the improvement is not significant to conclude these features are better at predicting human ratings.