Please use this identifier to cite or link to this item:
Title: Hierarchical (multi-label) architectural image recognition and classification
Authors: Chen, J
Stouffs, R 
Biljecki, F 
Issue Date: 1-Jan-2021
Citation: Chen, J, Stouffs, R, Biljecki, F (2021-01-01). Hierarchical (multi-label) architectural image recognition and classification 1 : 161-170. ScholarBank@NUS Repository.
Abstract: 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.
ISBN: 9789887891758
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
caadria2021_039.pdfPublished version6.94 MBAdobe PDF



Page view(s)

checked on Sep 22, 2022


checked on Sep 22, 2022

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.