Details
| Original language | English |
|---|---|
| Pages (from-to) | 174 - 192 |
| Journal | International Journal of Architectural Computing |
| Volume | 23 |
| Issue number | 1 |
| Early online date | 10 Sept 2024 |
| Publication status | Published - Mar 2025 |
Abstract
This paper introduces a computational aesthetics framework utilizing computer vision (CV) and artificial neural networks (ANN) to predict the aesthetic preferences of groups of people for architecture. It relies on part-to-whole theories from aesthetics and cognitive psychology. A survey of a group of people on preferences of images is held to record an average hedonic response (AHR). CV algorithms MSER and SAM recognize parts in images. Birkhoff’s aesthetic measure formula is adapted by employing the number of parts and their connections. These quantities are used as input layers of an ANN, and the AHR is the target output. The ANN evaluates images to output a predicted hedonic response (PHR), which is tested as a criterion in parametric design space navigation and in mapping the latent space of GANs. We conclude that such a framework is a heuristic method for better understanding the design and latent spaces and exploring designs.
Keywords
- Artificial neural networks, computational aesthetics, computer vision, design space navigation, empirical aesthetics, hedonic response, heuristics, latent space map, quantitative aesthetics
ASJC Scopus subject areas
- Engineering(all)
- Building and Construction
- Computer Science(all)
- Computer Science Applications
- Computer Science(all)
- Computer Graphics and Computer-Aided Design
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In: International Journal of Architectural Computing, Vol. 23, No. 1, 03.2025, p. 174 - 192.
Research output: Contribution to journal › Article › Research › peer review
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TY - JOUR
T1 - A computational framework for aesthetic preferences in architecture using computer vision and artificial neural networks
AU - Sardenberg, Victor
AU - Guatelli, Igor
AU - Becker, Mirco
N1 - Publisher Copyright: © The Author(s) 2024.
PY - 2025/3
Y1 - 2025/3
N2 - This paper introduces a computational aesthetics framework utilizing computer vision (CV) and artificial neural networks (ANN) to predict the aesthetic preferences of groups of people for architecture. It relies on part-to-whole theories from aesthetics and cognitive psychology. A survey of a group of people on preferences of images is held to record an average hedonic response (AHR). CV algorithms MSER and SAM recognize parts in images. Birkhoff’s aesthetic measure formula is adapted by employing the number of parts and their connections. These quantities are used as input layers of an ANN, and the AHR is the target output. The ANN evaluates images to output a predicted hedonic response (PHR), which is tested as a criterion in parametric design space navigation and in mapping the latent space of GANs. We conclude that such a framework is a heuristic method for better understanding the design and latent spaces and exploring designs.
AB - This paper introduces a computational aesthetics framework utilizing computer vision (CV) and artificial neural networks (ANN) to predict the aesthetic preferences of groups of people for architecture. It relies on part-to-whole theories from aesthetics and cognitive psychology. A survey of a group of people on preferences of images is held to record an average hedonic response (AHR). CV algorithms MSER and SAM recognize parts in images. Birkhoff’s aesthetic measure formula is adapted by employing the number of parts and their connections. These quantities are used as input layers of an ANN, and the AHR is the target output. The ANN evaluates images to output a predicted hedonic response (PHR), which is tested as a criterion in parametric design space navigation and in mapping the latent space of GANs. We conclude that such a framework is a heuristic method for better understanding the design and latent spaces and exploring designs.
KW - Artificial neural networks
KW - computational aesthetics
KW - computer vision
KW - design space navigation
KW - empirical aesthetics
KW - hedonic response
KW - heuristics
KW - latent space map
KW - quantitative aesthetics
UR - http://www.scopus.com/inward/record.url?scp=85203713801&partnerID=8YFLogxK
U2 - 10.1177/14780771241279350
DO - 10.1177/14780771241279350
M3 - Article
VL - 23
SP - 174
EP - 192
JO - International Journal of Architectural Computing
JF - International Journal of Architectural Computing
SN - 1478-0771
IS - 1
ER -