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Linear kitchen layout design via machine learning

Part of: Perspectives on Data-Enabled Design - Design meets Data Science

Published online by Cambridge University Press:  09 February 2022

Jelena Pejic  and
Petar Pejic Open the ORCID record for Petar Pejic [Opens in a new window]
Jelena Pejic
Affiliation:
Computer Science Department, Faculty of Sciences and Mathematics, University of Nis, Nis, Serbia
Petar Pejic*
Affiliation:
Game Development Department, Faculty of Information Technology, Belgrade Metropolitan University, Belgrade, Serbia
*
Author for correspondence: Petar Pejic, E-mail: petar.pejic@metropolitan.ac.rs
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Abstract

The main objective of this paper is to develop a novel approach for linear kitchen layout design which utilizes information from existing layouts via machine learning algorithms. With the growing popularity of large-scale virtual 3D environments for architectural visualization and the game industry, the manual interior design of virtual scenes becomes prohibitively expensive in terms of time and resources. In our approach, the machine learning model automatically generates layout suggestions. The proposed procedural kitchen generation (PKG) model is a pipeline of six Machine Learning (ML) classifiers that are trained and tested on a kitchen layout dataset created by interior designers. The performances of the model are evaluated for the following classifiers: Random forest, Decision tree, AdaBoost, Naive Bayes, MLP, SVM, and L2 Logistic regression. Random forest, as the best performing classifier is used in the final PKG model, and integrated into Unity Engine for automatic 3D kitchen generation and presentation. The PKG model is evaluated in the quantitative and perceptual study, showing better performance than the prior rule-based method. The perceptual study results demonstrate that our tool can be used to speed up designer's work, improve communication with clients, and educate interior design students.

Keywords

Data-driven modeldata-enabled approachinterior designkitchen layout designmachine learningprocedural content generationrandom forest
Type
Research Article
Information
AI EDAM , Volume 36 , 2022 , e9
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Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

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