| id |
caadria2024_501 |
| authors |
Cai, Chenyi, Wang, Xiao, Li, Biao and Herthogs, Pieter |
| year |
2024 |
| title |
ArchiSearch: A Text-Image Integrated Case-based Search Engine in Architecture Using Self-organizing Maps |
| doi |
https://doi.org/10.52842/conf.caadria.2024.3.019
|
| source |
Nicole Gardner, Christiane M. Herr, Likai Wang, Hirano Toshiki, Sumbul Ahmad Khan (eds.), ACCELERATED DESIGN - Proceedings of the 29th CAADRIA Conference, Singapore, 20-26 April 2024, Volume 3, pp. 19–28 |
| summary |
Case-based study and reasoning are considered fundamental meta-practices of architectural design. There are many online platforms to share architectural projects, which serve as data sources for case studies. However, search and retrieval capabilities offered by such platforms often do not cater to the professional needs of architects and do not integrate or synthesize the semantics present in text and images. We propose a systematic methodology to develop a search engine for architectural and urban projects using text and image data from one such online project-sharing platform: Chinese platform Gooood. Our approach automatically collects and extracts features from data, and integrates figurative and descriptive retrieving methods using word2vec, deep learning, and clustering algorithms. Our methodology provides a flexible approach for developing case-based search engines for architectural projects that take into consideration both images and texts and could be applied to any (Chinese language) platform. It offers architects augmented case-based workflows, which enrich design inspiration and accelerate decision-making meta-practices by unlocking the semantic search and retrieval of existing projects in novel ways. |
| keywords |
case-based design, text-image semantic search, case representation, self-organizing map |
| series |
CAADRIA |
| email |
chenyi.cai@sec.ethz.ch |
| full text |
 file.pdf (3,425,123 bytes) |
| references |
Content-type: text/plain
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| last changed |
2024/11/17 22:05 |
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