Approach to the extraction of design features of interior design elements using image recognition technique

Jin Sung Kim, Jae Yeol Song, Jin Kook Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationCAADRIA 2018 - 23rd International Conference on Computer-Aided Architectural Design Research in Asia
Subtitle of host publicationLearning, Prototyping and Adapting
EditorsSuleiman Alhadidi, Tomohiro Fukuda, Weixin Huang, Patrick Janssen, Kristof Crolla
PublisherThe Association for Computer-Aided Architectural Design Research in Asia (CAADRIA)
Pages287-296
Number of pages10
ISBN (Electronic)9789887891703
Publication statusPublished - 2018
Event23rd International Conference on Computer-Aided Architectural Design Research in Asia: Learning, Prototyping and Adapting, CAADRIA 2018 - Beijing, China
Duration: 2018 May 172018 May 19

Publication series

NameCAADRIA 2018 - 23rd International Conference on Computer-Aided Architectural Design Research in Asia: Learning, Prototyping and Adapting
Volume2

Other

Other23rd International Conference on Computer-Aided Architectural Design Research in Asia: Learning, Prototyping and Adapting, CAADRIA 2018
CountryChina
CityBeijing
Period18/5/1718/5/19

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea Grantfunded by the Korean Government (NRF-2015R1C1A1A01053497).

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design
  • Building and Construction

Fingerprint Dive into the research topics of 'Approach to the extraction of design features of interior design elements using image recognition technique'. Together they form a unique fingerprint.

Cite this