A computational framework for aesthetic preferences in architecture using computer vision and artificial neural networks

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OriginalspracheEnglisch
FachzeitschriftInternational Journal of Architectural Computing
PublikationsstatusElektronisch veröffentlicht (E-Pub) - 10 Sept. 2024

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.

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A computational framework for aesthetic preferences in architecture using computer vision and artificial neural networks. / Sardenberg, Victor; Guatelli, Igor; Becker, Mirco.
in: International Journal of Architectural Computing, 10.09.2024.

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Sardenberg, V., Guatelli, I., & Becker, M. (2024). A computational framework for aesthetic preferences in architecture using computer vision and artificial neural networks. International Journal of Architectural Computing. Vorabveröffentlichung online. https://doi.org/10.1177/14780771241279350
Sardenberg V, Guatelli I, Becker M. A computational framework for aesthetic preferences in architecture using computer vision and artificial neural networks. International Journal of Architectural Computing. 2024 Sep 10. Epub 2024 Sep 10. doi: 10.1177/14780771241279350
Sardenberg, Victor ; Guatelli, Igor ; Becker, Mirco. / A computational framework for aesthetic preferences in architecture using computer vision and artificial neural networks. in: International Journal of Architectural Computing. 2024.
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