| id |
ecaade2022_75 |
| authors |
Sardenberg, Victor and Becker, Mirco |
| year |
2022 |
| title |
Computational Quantitative Aesthetics Evaluation - Evaluating architectural images using computer vision, machine learning and social media |
| doi |
https://doi.org/10.52842/conf.ecaade.2022.2.567
|
| source |
Pak, B, Wurzer, G and Stouffs, R (eds.), Co-creating the Future: Inclusion in and through Design - Proceedings of the 40th Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2022) - Volume 2, Ghent, 13-16 September 2022, pp. 567–574 |
| summary |
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. |
| keywords |
Quantitative Aesthetics, Crowdsourcing, Aesthetic Measure, Computer Vision, Machine Learning, Social Media |
| series |
eCAADe |
| email |
|
| full text |
 file.pdf (964,810 bytes) |
| references |
Content-type: text/plain
|
| last changed |
2024/04/22 07:10 |
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