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Machine Vision and Algorithmic Creativity: Application of Image Recognition Algorithms to Architectural Style Analysis

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This chapter presents a practice-based investigation into the potential of machine vision as a tool for architectural analysis and critique in heritage studies. Drawing from an original experimental project, the study focuses on the application of DeepDream, a convolutional neural network visualisation technique, to the analysis of architectural styles. In doing so it explores how synthetic cognition perceives and represents stylistic differences across four image datasets: Gothic, Renaissance, Baroque, and Modernist architecture. Rather than producing new forms, the algorithm generates abstract, depthless compositions that expose latent patterns evidencing the uncanny experience of non-optical vision. Situated at the intersection of architectural theory and data philosophy, the chapter frames algorithmic gaze as a critical lens through which to rethink representation, authorship, and style in the age of non-conscious cognition. The findings suggest a new methodological framework for architectural historians and designers to engage with artificial intelligence as a collaborator in visual analysis.

Keywords: Algorithmic Creativity ; Architectural Style Analysis ; Artificial Intelligence ; Convolutional Neural Networks (CNNs) ; DeepDream ; Digital Heritage ; Generative AI ; Image Recognition ; Machine Learning (ML) ; Machine Vision

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Machine Vision and Algorithmic Creativity: Application of Image Recognition Algorithms to Architectural Style Analysis
Intellect , 213 (2026); https://doi.org/10.1386/9781835952962_12
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References

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  2. Baudry, Jean-Louis and Williams, Alan (1974), ‘Ideological effects of the basic cinematographic apparatus’, Film Quarterly, 28:2, pp. 39–47.
     [Google Scholar]
  3. Borji, Ali (2023), ‘Generated faces in the wild: Quantitative comparison of Stable Diffusion, Midjourney and DALL-E 2’, arXiv preprint, arXiv:2210.00586. Accessed 16 November 2024.
  4. Carpo, Mario and Davidson, Cynthia (2017), The Second Digital Turn: Design Beyond Intelligence, Cambridge, MA: MIT Press.
  5. Crary, Jonathan ([1999] 2001), Suspensions of Perception: Attention, Spectacle, and Modern Culture, Cambridge, MA: MIT Press.
  6. Deng, Jia, Dong, Wei, Socher, Richard, Li, Li-Jia, Li, Kai and Li, Fei-Fei (2009), ‘ImageNet: A large-scale hierarchical image database’, in 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Miami, FL, 20–25 June, pp. 248–55, https://doi.org/10.1109/CVPR.2009.5206848. Institute of Electrical and Electronics Engineers (IEEE).
     [Google Scholar]
  7. Evans, Robin ([1995] 2000), The Projective Cast: Architecture and Its Three Geometries, Cambridge, MA: MIT Press.
  8. Goodfellow, Ian, Bengio, Yoshua, Courville, Aaron and Bach, Franci (2017), Deep Learning, Cambridge, MA: MIT Press.
  9. Heardman, Adam (2018), ‘The real future of art and artificial intelligence, with Mario Klingemann and Anna Ridler’, MutualArt, 2 November, https://www.mutualart.com/Article/The-Real-Future-of-Art-and-Artificial-In/D741A0C0C602F7E5. Accessed 16 November 2024.
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  10. Jay, Martin (1988), ‘Scopic regimes of modernity’, in H. Foster (ed.), Vision and Visuality: Discussions in Contemporary Culture, Seattle, WA: Bay Press, pp. 3–27.
     [Google Scholar]
  11. Jay, Martin (1993), Downcast Eyes: The Denigration of Vision in Twentieth-Century French Thought, Berkeley, CA: University of California Press.
  12. Krizhevsky, Alex, Sutskever, Ilya and Hinton, Geoffrey (2012), ‘ImageNet classification with deep convolutional neural networks’, Communications of the ACM, 60:6, pp. 84–90, https://doi.org/10.1145/3065386.
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  13. Leach, Neil (2022), Architecture in the Age of Artificial Intelligence: An Introduction to AI for Architects, London: Bloomsbury Publishing.
  14. LeCun, Yann, Bottou, Léon, Bengio, Yoshua and Haffner, Patrick (1998), ‘Gradient-based learning applied to document recognition’, Proceedings of the IEEE, 86:11, pp. 2278–324, https://doi.org/10.1109/5.72679.
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  18. Pasquinelli, Matteo (2018), ‘Metadata society’, in R. Braidotti and M. Hlavajova (eds), Posthuman Glossary, London: Bloomsbury, pp. 253–55.
     [Google Scholar]
  19. Pasquinelli, Matteo (2019), ‘How a machine learns and fails: A grammar of error for artificial intelligence’, Spheres: Journal for Digital Cultures, 5, pp. 1–15, https://mediarep.org/handle/doc/14413. Accessed 16 November 2024.
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  20. Pasquinelli, Matteo and Joler, Vladan (2021), ‘The Nooscope manifested: AI as instrument of knowledge extractivism’, AI & Society, 36, pp. 1263–80, https://doi.org/10.1007/s00146-020-01097-6.
     [Google Scholar]
  21. Rani, Sunanda, Jining, Dong, Shah, Dhaneshwar, Xaba, Siyanda and Shoukat, Khadija (2024), ‘Examining the impacts of artificial intelligence technology and computing on digital art: A case study of Edmond de Belamy and its aesthetic values and techniques’, AI & Society, June, https://doi.org/10.1007/s00146-024-01996-y.
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  22. Ridler, Anna (2017), ‘GANs in art’, Misremembering and Mistranslating: GANs in an Art Context, 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA, http://annaridler.com/gans-inart. Accessed 16 November 2024.
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  23. Rouvroy, Antoinette and Berns, Thomas (2013), ‘Algorithmic governmentality and prospects of emancipation disparateness as a precondition for individuation through relationships?’ (trans. L. Carey-Libbrecht), Réseaux, 177:1, pp. 163–96, https://shs.cairn.info/journal-reseaux-2013-1-page-163?lang=en. Accessed 16 November 2024.
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  24. Rouvroy, Antoinette and Stiegler, Bernard (2016), ‘The digital regime of truth: From the algorithmic governmentality to a new rule of law’ (trans. A. Nony and B. Dillet), La Deluziana, Life and Number, 3, pp. 6–29.
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  25. Russakovsky, Olga, Deng, Jia, Su, Hao, Krause, Jonathan, Satheesh, Sanjeev, Ma, Sean, Huang, Zhiheng Huang, et al.. (2015), ‘ImageNet large scale visual recognition challenge’, International Journal of Computer Vision, 115:2015, pp. 211–52.
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  26. Russell, Stuart and Norvig, Peter (2020), Artificial Intelligence: A Modern Approach, Hoboken, NJ: Pearson.
  27. Samuel, Arthur L. (1959), ‘Some studies in machine learning using the game of checkers’, IBM Journal of Research and Development, 3:3, pp. 210–29, https://doi.org/10.1147/rd.33.0210.
     [Google Scholar]
  28. Silvestre, Joaquim, Ikeda, Yasushi and Guéna, François, ‘Artificial imagination of architecture with deep convolutional neural network “Laissez-Faire”: Loss of control in the esquisse phase’, in Proceedings of the CAADRIA 2016, 21st International Conference on Computer-Aided Architectural Design Research in Asia – Living Systems and Micro-Utopias: Towards Continuous Designing, Melbourne, Australia, 30 March – 2 April, pp. 881–90.
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