Computational Quantitative Aesthetics Evaluation: Evaluating architectural images using computer vision, machine learning and social media

Abstract

This paper correlates two methods of aesthetic evaluation of architectural images utilising computer vision (CV) and machine learning (ML) for automating aesthetic evaluation: Calibrated aesthetic measure (CalAM) and aesthetic scoring model (ASM). From a database of images of proposals for a single location, users are invited to like or dislike it on social media to feed an ML model and calibrate an aesthetic measure formula (AMF). A possible application is to assist designers in making decisions according to the hedonic response given by users previously, enabling a faster way of popular participation.