Approach to Design Reference Management using Auto-Recognition System of Room and Design Style

  • Authors

    • Jin-Sung Kim
    • Jung-Sik Choi
    • Jin-Kook Lee
    https://doi.org/10.14419/ijet.v8i1.4.25133

    Received date: December 31, 2018

    Accepted date: December 31, 2018

    Published date: January 2, 2019

  • Design reference image, Deep learning, Image recognition, Automatic classification, Interior Design
  • Abstract

    This paper aims to propose an approach to managing interior design reference images using the automatic recognition system for room and design style. In practice, architects, designers, and other interested parties actively use web-based platforms for which design references are provided with a well-organized information structure. In particular, the photograph has the role of a primary source to support analyze, and understand architectural design. However, the current management approach consumes significant time and design expertise resources. In addition, the approach to managing and labeling filenames that are irrelevant to the image itself is inefficient and can lead to errors. Therefore, we use a deep learning mechanism and pre-trained image recognition model to retrain the model with the Korean apartment room and its design style. Using image recognition technique, it is possible to classify design images with only visual pixel data and measure the visual similarity. We developed a prototype GUI application that supports checking the result of the automatic recognition and management. In addition, the reference image data can be effectively searched and utilized through more specific search functions and both stochastic keyword-based and visual similarity-based retrieval.

  • References

    1. Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, Ghemawat S, Irving G & Isard M,"TensorFlow: A System for Large-Scale Machine Learning", OSDI, 16, (2016), pp.265-283
    2. Akin Ö, Psychology of architectural design, Pion Limited, (1986),
    3. Clark RH & Pause M, Precedents in architecture: analytic diagrams, formative ideas, and partis, John Wiley & Sons, (2012),
    4. Cross N, Designerly ways of knowing, Springer, (2006),
    5. Domeshek EA & Kolodner JL, A case-based design aid for architecture, Artificial Intelligence in Design’92, Springer, (1992), pp.497-516
    6. Eastman C, Lee J-m, Jeong Y-s & Lee J-k, (2009), Automatic rule-based checking of building designs, Automation in construction, Vol.18, No.8, pp.1011-1033
    7. Eilouti BH (2009), Design knowledge recycling using precedent-based analysis and synthesis models, Design Studies, Vol.30, No.4, pp.340-368
    8. Kang TW, Kim JE & Choi HS (2016), Effectiveness Analysis of BIM-based Architectural Facility Management Scenario, Journal of the Korea Academia-Industrial cooperation Society, Vol.17, No.11, pp.10-19
    9. Kim HY & Lee JK (2018), Development of the Logic-Rule based Approach to the Computer-readable Building Permit-Related Code Sentences for the Automated Code-Compliance Checking, Korean Journal of Computational Design and Engineering, Vol.23, No.2, pp.127-136
    10. Kim HY & Lee JK (2016), Relational Logic Definition of Articles and Sentences in Korean Building Code for the Automated Building Permit System, Korean Journal of Computational Design and Engineering, Vol.21, No.4, pp.433-442
    11. Kim JS, Kim H & Lee JK,"Deep-learning based Auto-classifying Design Style of Interior Image", Proceedings of Korean Institute of Interior Design Conference, 19, (2017), pp.95-98
    12. Koutamanis A & Mitossi V (1993), Computer vision in architectural design, Design Studies, Vol.14, No.1, pp.40-57
    13. Krizhevsky A, Sutskever I & Hinton GE,"Imagenet classification with deep convolutional neural networks", Advances in neural information processing systems (2012), pp.1097-1105
    14. Lawson B, How designers think: the design process demystified, Routledge, (2006),
    15. LeCun Y, Bengio Y & Hinton G (2015), Deep learning, nature, Vol.521, No.7553, pp.436-448
    16. Lee H, Lee JK, Park SK & Kim I (2016), Translating building legislation into a computer-executable format for evaluating building permit requirements, Automation in Construction, Vol.71, pp.49-61
    17. Maaten Lvd & Hinton G (2008), Visualizing data using t-SNE, Journal of machine learning research, Vol.9, No.Nov, pp.2579-2605
    18. Mitchell WJ, The logic of architecture: Design, computation, and cognition, MIT press, (1990),
    19. Oh KS, Park SH & Song JW (2016), A Study on the Using of BIM Data and Template for Construction Progress Management, Journal of the Korea Academia-Industrial cooperation Society, Vol.17, No.8, pp.157-163
    20. Oxman RE, (1994) Precedents in design: a computational model for the organization of precedent knowledge, Design studies, Vol.15, No.2, pp.141-157
    21. Park SH & Hong CH (2017), A Study for BIM based Evaluation and Process for Architectural Design Competition - Case Study of Domestic and International BIM-based Competition, Journal of the Korea Academia-Industrial cooperation Society, Vol.18, No.2, pp.23-30
    22. Park SK, Lee JK & Kim IH (2016), Development of High-level Method for Representing Explicit Verb Phrases of Building Code Sentences for the Automated Building Permit System of Korea, Korean Journal of Computational Design and Engineering, Vol.21, No.3, pp.313-324
    23. Park SK, Lee YC & Lee JK (2016), Definition of a domain-specific language for Korean building act sentences as an explicit computable form, Journal of Information Technology in Construction (ITcon), Vol.21, No.26, pp.422-433
    24. Pearce M, Goel AK, Kolodner I, Zimring C, Sentosa L & Billington R (1992), Case-based design support: A case study in architectural design, IEEE expert, Vol.7, No.5, pp.14-20
    25. Rittel HW & Webber MM (1973), Dilemmas in a general theory of planning, Policy sciences, Vol.4, No.2, pp.155-169
    26. Schmitt G(1994), Case-based design and creativity, Automation Based Creative Design–Research and Perspectives, Elsevier, , pp.41-53
    27. Schön DA (1987), Educating the reflective practitioner: Toward a new design for teaching and learning in the professions, Jossey-Bass,
    28. Watson I & Perera S (1997), Case-based design: A review and analysis of building design applications, AI EDAM, Vol.11, No.1, pp.59-87
    29. The Oxford-IIIT Pet Dataset, http://robots.ox.ac.uk/~vgg/data/pets (Accessed: 18/06/2018).
    30. Archdaily, https:/archdaily.com/ (Accessed: 18/06/2018).
    31. Architizer, https://architizer.com/(Accessed: 18/06/2018).
    32. Dwell, http://dewell.com/ (Accessed: 18/06/2018).
    33. Houzz, https:// houzz.com/ (Accessed: 18/06/2018).
    34. Ohouse, https://ohou.se/ (Accessed: 18/06/2018).
    35. Zipdac, http://www.zipdoc.co.kr/ (Accessed: 18/06/2018).
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  • How to Cite

    Kim, J.-S., Choi, J.-S., & Lee, J.-K. (2019). Approach to Design Reference Management using Auto-Recognition System of Room and Design Style. International Journal of Engineering and Technology, 8(1.4), 56-64. https://doi.org/10.14419/ijet.v8i1.4.25133