This paper aims to propose deep learning-based approach to the auto-recognition of their design features of interior design elements using given digital images. The recently image recognition technique using convolutional neural networks has shown great success in the various field of research and industry. The open-source frameworks and pre-trained image recognition models supporting image recognition task enable us to easily retrain the models to apply them on any domain. This paper describes how to apply such techniques on interior design process and depicts some demonstration results in that approaches. Furniture that is one of the most common interior design elements has sub-feature including implicit design features, such as style, shape, function as well as explicit properties, such as component, materials, and size. This paper shows to retrain the model to extract some of the features for efficiently managing and utilizing such design information. The target element is chair and the target design features are limited to functional features, materials, seating capacity and design style. Total 3933 chair images dataset and 6 retrained image recognition models were utilized for retraining. Through the combination of those multiple models, inference demonstration also has been described.
|Title of host publication||CAADRIA 2018 - 23rd International Conference on Computer-Aided Architectural Design Research in Asia|
|Subtitle of host publication||Learning, Prototyping and Adapting|
|Editors||Suleiman Alhadidi, Tomohiro Fukuda, Weixin Huang, Patrick Janssen, Kristof Crolla|
|Publisher||The Association for Computer-Aided Architectural Design Research in Asia (CAADRIA)|
|Number of pages||10|
|Publication status||Published - 2018|
|Event||23rd International Conference on Computer-Aided Architectural Design Research in Asia: Learning, Prototyping and Adapting, CAADRIA 2018 - Beijing, China|
Duration: 2018 May 17 → 2018 May 19
|Name||CAADRIA 2018 - 23rd International Conference on Computer-Aided Architectural Design Research in Asia: Learning, Prototyping and Adapting|
|Other||23rd International Conference on Computer-Aided Architectural Design Research in Asia: Learning, Prototyping and Adapting, CAADRIA 2018|
|Period||18/5/17 → 18/5/19|
Bibliographical noteFunding Information:
This work was supported by the National Research Foundation of Korea Grantfunded by the Korean Government (NRF-2015R1C1A1A01053497).
© 2018 and published by the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA) in Hong Kong.
All Science Journal Classification (ASJC) codes
- Computer Graphics and Computer-Aided Design
- Building and Construction