Skip to main content
SpringerLink
Log in

PU learning-based recognition of structural elements in architectural floor plans

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

This work introduces a computational method for the recognition of structural elements in architectural floor plans. The proposed method requires minimal user interaction and is capable of effectively analysing floor plans in order to identify different types of structural elements in various notation styles. It employs feature extraction based on Haar kernels and PU learning, in order to retrieve image regions, which are similar to a user-defined query. Most importantly, apart from this user-defined query, the proposed method is not dependent on learning from labelled samples. Therefore, there is no need for laborious annotations to form large datasets in various notation styles. The experimental evaluation has been performed on a publicly available and diverse dataset of floor plans. The results show that the proposed method outperforms a state-of-the-art method, with respect to retrieval accuracy. Further experiments on additional floor plans of various notation styles, demonstrate its general applicability.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Ahmed S, Liwicki M, Weber M, Dengel A (2011) Improved automatic analysis of architectural floor plans. In: Proceedings ICDAR, pp 864–869, DOI https://doi.org/10.1109/ICDAR.2011.177, (to appear in print)

  2. Bay H, Ess A, Tuytelaars T, Gool LV (2008) Speeded-up robust features (SURF). Comput Vis Image Under 110(3):346–359. https://doi.org/10.1016/j.cviu.2007.09.014. Similarity matching in computer vision and multimedia

    Article  Google Scholar 

  3. Bekker J, Davis J (2020) Learning from positive and unlabeled data: A survey. Mach Learn 109:719–760

    Article  MathSciNet  Google Scholar 

  4. Bottou L (2010) Large-scale machine learning with stochastic gradient descent. In: Proceedings COMPSTAT

  5. Bradski G (2000) The openCV Library. Dr Dobb’s J Software Tools

  6. Chen L, Zhu Y, Papandreou G, Schroff F, Adam H (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings Eur conf comp vis (ECCV), vol 11211, pp 833–851

  7. Cherneff J, Logcher R, Connor J, Patrikalakis N (1992) Knowledge-based interpretation of architectural drawings. Res Engineer Design 3(4):195–210. https://doi.org/10.1007/BF01580842

    Article  Google Scholar 

  8. Claesen M, De Smet F, Suykens J, De Moor B (2015) A robust ensemble approach to learn from positive and unlabeled data using svm base models. Neurocomp 160:73–84. https://doi.org/10.1016/j.neucom.2014.10.081

    Article  Google Scholar 

  9. Delalandre M, Valveny E, Pridmore TP, Karatzas D (2010) Generation of synthetic documents for performance evaluation of symbol recognition & spotting systems. Int J Document Anal Recognit 13(3):187–207. https://doi.org/10.1007/s10032-010-0120-x

    Article  Google Scholar 

  10. De las Heras LP, Ahmed S, Liwicki M, Valveny E, Sánchez G (2014) Statistical segmentation and structural recognition for floor plan interpretation. Int J Doc Anal Rec 17(3):221–237. https://doi.org/10.1007/s10032-013-0215-2

    Article  Google Scholar 

  11. Dodge SF, Xu J, Stenger B (2017) Parsing floor plan images. In: IAPR Int conf mach vis appl, pp 358–361

  12. Dosch P, Tombre K, Ah-Soon C, Masini G (2000) A complete system for the analysis of architectural drawings. Int J Doc Anal Rec 3(2):102–116. https://doi.org/10.1007/PL00010901

    Article  Google Scholar 

  13. Gerstweiler G, Furlan L, Timofeev M, Kaufmann H (2018) Extraction of structural and semantic data from 2D floor plans for interactive and immersive VR real estate exploration. Technologies 6(101)

  14. Guo X, Peng Y (2018) Floor plan classification based on transfer learning. In: Proceedings ICCC, pp 1720–1724

  15. Kalervo A, Ylioinas J, Häikiö M, Karhu A, Kannala J (2019) Cubicasa5k: A dataset and an improved multi-task model for floorplan image analysis. In: Proceedings SCIA 2019, Lecture notes in computer science, vol 11482. Springer, pp 28–40, DOI https://doi.org/10.1007/978-3-030-20205-7_3

  16. Liu C, Wu J, Kohli P, Furukawa Y (2017) Raster-to-vector: Revisiting floorplan transformation. In: IEEE international conference on computer vision (ICCV), pp 2214–2222

  17. Liu P, Yu H, Cang S (2019) Adaptive neural network tracking control for underactuated systems with matched and mismatched disturbances. Nonlinear Dyn 98:1447–1464

    Article  Google Scholar 

  18. Lu T, Yang H, Yang R, Cai S (2007) Automatic analysis and integration of architectural drawings. Int J Doc Anal Rec 9(1):31–47. https://doi.org/10.1007/s10032-006-0029-6

    Article  Google Scholar 

  19. Macé S., Locteau H, Valveny E, Tabbone S (2010) A system to detect rooms in architectural floor plan images. In: Proceedings DAS, pp 167–174, DOI https://doi.org/10.1145/1815330.1815352, (to appear in print)

  20. Mewada H, Pattel A, Chaudhari J, Mahant K, Vala A (2020) Automatic room information retrieval and classification from floor plan using linear regressio nmodel. Int J Doc Anal Rec

  21. Mordelet F, Vert JP (2014) A bagging svm to learn from positive and unlabeled examples. Patt Rec Lett 37:201–209. https://doi.org/10.1016/j.patrec.2013.06.010

    Article  Google Scholar 

  22. Or SH, Wong K, Yu Y, Chang MM (2005) Highly automatic approach to architectural floorplan image understanding & model generation. Patt Rec 25–32

  23. Ryall K, Shieber S, Marks J, Mazer M (1995) Semi-automatic delineation of regions in floor plans. In: Proceedings ICDAR, vol. 2, pp 964–969 vol.2

  24. Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: Bengio Y, LeCun Y (eds) Proceedings ICLR

  25. Sun L, Zhao C, Yan Z, Liu P, Duckett T, Stolkin R (2019) A novel weakly-supervised approach for RGB-d-based nuclear waste object detection. IEEE Sensors 19(9):3487–3500

    Article  Google Scholar 

  26. Tang Z, Li C, Wu J, Liu P, Cheng S (2019) Classification of eeg-based single-trial motor imagery tasks using a b-CSP method for BCI. Frontiers Inf Technol Electronic Eng 20:1087–1098

    Article  Google Scholar 

  27. Tang Z, Yu H, Lu C, Liu P, Jin X (2019) Single-trial classification of different movements on one arm based on ERD/ERS and corticomuscular coherence. IEEE Access 7 128185–128197

  28. Toussaint G (1983) Solving geometric problems with the rotating calipers

  29. Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. In: Proceedings CVPR, vol 1, pp I–I

  30. Weber M, Liwicki M, Dengel A. (2010) a.SCAtch - a sketch-based retrieval for architectural floor plans. In: Proceedings ICFHR, pp 289–294, DOI https://doi.org/10.1109/ICFHR.2010.122, (to appear in print)

  31. Wessel R, Blümel I, Klein R (2008) The room connectivity graph: Shape retrieval in the architectural domain. In: Proceedings ICFHR, DOI https://doi.org/10.1109/ICFHR.2010.122, (to appear in print)

  32. Yamasaki T, Zhang J, Takada Y (2018) Apartment structure estimation using fully convolutional networks and graph model. In: Proceedings ACM workshop on multimedia for real estate tech, pp 1–6

  33. Zeng Z, Li X, Yu YK, Fu C (2019) Deep floor plan recognition using a multi-task network with room-boundary-guided attention. In: Proceedings ICCV, pp 9095–9103. IEEE, DOI https://doi.org/10.1109/ICCV.2019.00919, (to appear in print)

  34. Zhao H, Shi J, Qi X, Wang X, Jia J (2017) Pyramid scene parsing network. In: Proceedings conference on computer vision and pattern recognition (CVPR), pp 6230–6239

  35. Ziran Z, Marinai S (2018) Object detection in floor plan images. In: Pancioni L, Schwenker F, Trentin E (eds) Proceedings ANNPR, lecture notes in computer science, vol 11081. Springer, pp 383–394, DOI https://doi.org/10.1007/978-3-319-99978-4_30

Download references

Acknowledgements

The authors would like to thank Apostolis Chatzisymeon and George Chatzisymeon of Nomitech LTD for initial discussions with respect to the problem, as well as for the provision of floor plans.

Author information

Authors and Affiliations

  1. Department of Informatics, Athens University of Economics and Business, Athens, Greece

    Iordanis Evangelou & Georgios Papaioannou

  2. Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece

    Michalis Savelonas

Authors
  1. Iordanis Evangelou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michalis Savelonas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Georgios Papaioannou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Michalis Savelonas.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Evangelou, I., Savelonas, M. & Papaioannou, G. PU learning-based recognition of structural elements in architectural floor plans. Multimed Tools Appl 80, 13235–13252 (2021). https://doi.org/10.1007/s11042-020-10295-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-10295-9

Keywords

Search

Navigation