Skip to main content

Advertisement

SpringerLink
Log in

Supervised training database for building recognition by using cross ratio invariance and SVD-based method

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

This paper describes an approach to training a database of building images under the supervision of a user. Then it will be applied to recognize buildings in an urban scene. Given a set of training images, we first detect the building facets and calculate their properties such as area, wall color histogram and a list of local features. All facets of each building surface are used to construct a common model whose initial parameters are selected randomly from one of these facets. The common model is then updated step-by-step by spatial relationship of remaining facets and SVD-based (singular value decomposition) approximative vector. To verify the correspondence of image pairs, we proposed a new technique called cross ratio-based method which is more suitable for building surfaces than several previous approaches. Finally, the trained database is used to recognize a set of test images. The proposed method decreases the size of the database approximately 0.148 times, while automatically rejecting randomly repeated features from the scene and natural noise of local features. Furthermore, we show that the problem of multiple buildings was solved by separately analyzing each surface of a building.

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

Similar content being viewed by others

References

  1. Bay H, Tuytelaars T, Gool LV (2006) SURF: speeded up robust features. In: LNCS on ECCV, vol 3951, pp 404–417

  2. MJ Black, Jepson AD (1998) EigenTracking: robust matching and tracking of articulated objects using a view-based representation. IJCV 26(1):63–84

    Article  Google Scholar 

  3. Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with application to image analysis and automated cartography. Commun ACM 24(6):381–395

    Article  MathSciNet  Google Scholar 

  4. Fritz G, Seifert C, Paletta L (2005) Urban object recognition from informative local features. In: Proc of IEEE int’l conf on ICRA, pp 131–137

  5. Fritz G, Seifert C, Paletta L (2006) A mobile vision system for urban detection with informative local descriptors. In: Proc of 4th IEEE int’l conf on ICVS, p 30

  6. Groeneweg NJC, de Groot B, Halma AHR, Quiroga BR, Tromp M, Groen FCA (2006) A fast offline building recognition application on a mobile telephone. In: Proc of IEEE int’l conf on ACIVS, pp 1122–1132

  7. Hartley R, Zisserman A (2004) Multiple view geometry in computer vision. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  8. Ke Y, Sukthankar R (2004) PCA-SIFT: a more distinctive representation for local image descriptors. In: Proc of IEEE computer society conf on CVPR, vol 2, pp 506–513

  9. Lazebnik S, Schmid S, Ponce J (2005) A sparse texture representation using local affine regions. IEEE Trans Pattern Anal Mach Intell 27(8):1265–1278

    Article  Google Scholar 

  10. Lowe DG (1999) Object recognition from local scale-invariant features. In: Proc of int’l conf on ICCV, pp 1150–1157

  11. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. IJCV 60:91–110

    Article  Google Scholar 

  12. Matas J, Obdrzalek S (2004) Object recognition methods based on transformation covariant features. In: Proc of European signal processing conf on EUSIPCO

  13. Marszalek M, Schmid C (2006) Spatial weighting for bag-of-features. In: Proc of IEEE computer society conf on computer vision and pattern recognition, vol 2, pp 2118–2125

  14. Mikolajczyk K, Schmid S (2003) A performance evaluation of local descriptors. In: Proc of IEEE computer society conf on CVPR, vol 02, p 257

  15. Rousseeuw PJ, Leroy AM (2003) Robust regression and outlier detection. Wiley InterScience, New York

    Google Scholar 

  16. Rothganger F, Lazebnik S, Schmid C, Ponce J (2006) 3D object modeling and recognition using local affine-invariant image descriptors and multi-view spatial constraints. IJCV 66(3):231–259

    Article  Google Scholar 

  17. Schaffalitzky F, Zisserman A (2002) Multi-view matching for unordered image sets, or ‘how do i organize my holiday snaps?’ In: ECCV02, pp 414–431

  18. Schindler G, Krishnamurthy P, Lublinerman R Liu Y, Dellaert F (2008) Detecting and matching repeated patterns for automatic geo-tagging in urban environments. In: Proc of IEEE comp soc conf on computer vision and pattern recognition (CVPR)

  19. Shao H, Svoboda T, Gool LV (2003) Zubud-zurich buildings database for image based recognition. Swiss FI of Tech, Tech report No 260

  20. Steinhoff U, Omercevic D, Perko R, Schiele B, Leonardis A (2007) How computer vision can help in outdoor positioning. In: LNCS on ambient intelligence, European conference, vol 4794, pp 124–141

  21. Swets, Weng J (1996) Using discriminant eigenfeatures for image retrieval. IEEE Trans Pattern Anal Mach Intell 18(8):831–836

    Article  Google Scholar 

  22. Torr PHS, Zisserman A (2000) MLESAC: a new robust estimator with application to estimating image geometry. CVIU 78:138–156

    Google Scholar 

  23. Torr PHS, (2002) A structure and motion toolkit in Matlab “interactive adventures in S and M”. Technical report MSR-TR-2002-56

  24. Trinh HH, Jo KH (2006) Image-based structural analysis of building using line segments and their geometrical vanishing points, SICE-ICASE

  25. Trinh HH, Kim DN, Jo KH (2007) Structure analysis of multiple building for mobile robot intelligence. In: Proc of int’l conf on SICE, Japan

  26. Trinh HH, Kim DN, Jo KH (2007) Urban building detection and analysis by visual and geometrical features. In: Proc of int’l conf on ICCAS07, Seoul, Korea

  27. Trinh HH, Kim DN, Jo KH (2008) Building surface refinement using cluster of repeated local features by cross ratio. J Lect Notes Artif Intell 5027:22–31

    Google Scholar 

  28. Trinh HH, Kim DN, Jo KH (2008) Facet-based multiple building analysis for robot intelligence. J Appl Math Comput 205(2):537–549

    Article  MATH  Google Scholar 

  29. Willamowski J, Arregui D, Csurka G, Dance CR, Fan F (2007) Categorizing nine visual classes using local appearance descriptors. In: Proc of the 6th ACM int’l conf on image and video retrieval, pp 242–249

  30. Zhang J, Marszalek M, Lazebnik S, Schmid C (2007) Local features and kernels for classification of texture and object categories: a comprehensive study. IJCV 73(2):213–238

    Article  Google Scholar 

  31. Zhang W, Kosecka J (2007) Hierarchical building recognition. IVC 25:704–716

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Graduate School of Electrical Engineering, University of Ulsan, Korea, Daehak-ro 102, Mugeo-Dong, Nam-Ku, Ulsan, 680-749, Korea

    Hoang-Hon Trinh, Dae-Nyeon Kim & Kang-Hyun Jo

Authors
  1. Hoang-Hon Trinh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dae-Nyeon Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kang-Hyun Jo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kang-Hyun Jo.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Trinh, HH., Kim, DN. & Jo, KH. Supervised training database for building recognition by using cross ratio invariance and SVD-based method. Appl Intell 32, 216–230 (2010). https://doi.org/10.1007/s10489-010-0221-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-010-0221-8

Keywords

Search

Navigation