Beauty in the Eye of Machine

Beauty in the Eye of Machine: Using Automated Measures of Aesthetic Beauty to Improve GAN Output of Satellite Images

Catlett, Joseph (TU Delft Electrical Engineering, Mathematics and Computer Science)

Lomas, J.D. (mentor)
van der Maden, W.L.A. (mentor)
Gadiraju, Ujwal (mentor)
Allen, G.M. (mentor)
Epema, D.H.J. (graduation committee)

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2022-06-23

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.

http://resolver.tudelft.nl/uuid:09486d4d-0aaf-4404-b8bc-ddb68e4f4d2d

Student theses

bachelor thesis

© 2022 Joseph Catlett