| id |
caadria2021_039 |
| authors |
Chen, Jielin, Stouffs, Rudi and Biljecki, Filip |
| year |
2021 |
| title |
Hierarchical (multi-label) architectural image recognition and classification |
| doi |
https://doi.org/10.52842/conf.caadria.2021.1.161
|
| source |
A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 161-170 |
| summary |
The task of architectural image recognition for both architectural functionality and style remains an open challenge. In addition, the paucity of well-organized, large-scale architectural image datasets with specific consideration for the domain of architectural design research has hindered the exploration of these challenging tasks. Drawing upon images from the professional architectural website Archdaily®, and leveraging state-of-the-art deep-learning-based classification models, we explore a hierarchical multi-label classification model as a potential baseline for the task of architectural image classification. The resulting model showcases the potential for innovative architectural discipline-related analyses and demonstrates some heuristic insights for visual feature extraction pertaining to both architectural functionality and architectural style. |
| keywords |
image recognition; hierarchical classification; multi-label classification; architectural functionality; style |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (7,110,086 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/06/07 07:55 |
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