Architectural Style Classification of Building Facade Windows

  • Gayane Shalunts
  • Yll Haxhimusa
  • Robert Sablatnig
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6939)

Abstract

Building facade classification by architectural styles allows categorization of large databases of building images into semantic categories belonging to certain historic periods, regions and cultural influences. Image databases sorted by architectural styles permit effective and fast image search for the purposes of content-based image retrieval, 3D reconstruction, 3D city-modeling, virtual tourism and indexing of cultural heritage buildings. Building facade classification is viewed as a task of classifying separate architectural structural elements, like windows, domes, towers, columns, etc, as every architectural style applies certain rules and characteristic forms for the design and construction of the structural parts mentioned. In the context of building facade architectural style classification the current paper objective is to classify the architectural style of facade windows. Typical windows belonging to Romanesque, Gothic and Renaissance/Baroque European main architectural periods are classified. The approach is based on clustering and learning of local features, applying intelligence that architects use to classify windows of the mentioned architectural styles in the training stage.

Keywords

Visual Word Interest Point Scale Invariant Feature Transform Architectural Style Codebook Size 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Zheng, Y.T., Zhao, M., Song, Y., Adam, H., Buddemeier, U., Bissacco, A., Brucher, F., Chua, T.S., Neven, H.: Tour the world: building a web-scale landmark recognition engine. In: Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 1085–1092 (2009)Google Scholar
  2. 2.
    Zhang, W., Kosecka, J.: Hierarchical building recognition. Image and Vision Computing 25(5), 704–716 (2004)CrossRefGoogle Scholar
  3. 3.
    Li, Y., Crandall, D., Huttenlocher, D.: Landmark classification in large-scale image collections. In: Proceedings of IEEE 12th International Conference on Computer Vision, pp. 1957–1964 (2009)Google Scholar
  4. 4.
    Cornelis, N., Leibe, B., Cornelis, K., Gool, L.V.: 3d urban scene modeling integrating recognition and reconstruction. International Journal of Computer Vision 78, 121–141 (2008)CrossRefGoogle Scholar
  5. 5.
    Snavely, N., Seitz, S.M., Szeliski, R.: Photo tourism: exploring photo collections in 3d. ACM Transaction on Graphics 25, 835–846 (2006)CrossRefGoogle Scholar
  6. 6.
    Ali, H., Seifert, C., Jindal, N., Paletta, L., Paar, G.: Window detection in facades. In: 14th International Conference on Image Analysis and Processing (ICIAP 2007). Springer, Heidelberg (2007)Google Scholar
  7. 7.
    Recky, M., Leberl, F.: Windows detection using k-means in cie-lab color space. In: Ünay, D., Çataltepe, Z., Aksoy, S. (eds.) ICPR 2010. LNCS, vol. 6388, pp. 356–360. Springer, Heidelberg (2010)Google Scholar
  8. 8.
    Recky, M., Leberl, F.: Window detection in complex facades. In: European Workshop on Visual Information Processing (EUVIP 2010), pp. 220–225 (2010)Google Scholar
  9. 9.
    Collins, P.: Changing Ideals in Modern Architecture, pp. 1750–1950. McGill-Queen’s University Press (1998)Google Scholar
  10. 10.
    Ojala, T., Pietikinen, M., Mäenpää, T.: Multiresolution grayscale and rotation invariant texture classification with local binary patterns. IEEE Trans. on Pattern Analysis and Machine Intelligence 24, 971–987 (2002)CrossRefGoogle Scholar
  11. 11.
    Haralick, R.M.: Statistical and structural approaches to texture. Proc. IEEE 67, 786–804 (1979)CrossRefGoogle Scholar
  12. 12.
    Zhang, D., Lu, G.: Review of shape representation and description techniques. Pattern Recognition 37, 1–19 (2004)CrossRefGoogle Scholar
  13. 13.
    Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. on Pattern Analysis and Machine Intelligence 24, 509–522 (2002)CrossRefGoogle Scholar
  14. 14.
    Crowley, J.L., Parker, A.C.: A representation for shape based on peaks and ridges in the difference of lowpass transform. IEEE Trans. on Pattern Analysis and Machine Intelligence 6(2), 156–170 (1984)CrossRefGoogle Scholar
  15. 15.
    Harris, C., Stephens, M.: A combined corner and edge detector. In: Proceedings of The Fourth Alvey Vision Conference, pp. 147–151 (1998)Google Scholar
  16. 16.
    Mikolajczyk, K., Schmid, C.: Indexing based on scale invariant interest points. In: Internationl Conference in Computer Vision, pp. 525–531 (2001)Google Scholar
  17. 17.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  18. 18.
    Matas, J., Chum, O., Urban, M., Pajdla1, T.: Robust wide baseline stereo from maximally stable extremal regions. In: British Machine Vision Conference, pp. 384–393 (2002)Google Scholar
  19. 19.
    Tuytelaars, T., Gool, L.V.: Wide baseline stereo matching based on local, affinely invariant regions. In: British Machine Vision Conference, pp. 412–425 (2000)Google Scholar
  20. 20.
    Csurka, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: ECCV 2004, pp. 1–22 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Gayane Shalunts
    • 1
  • Yll Haxhimusa
    • 2
  • Robert Sablatnig
    • 1
  1. 1.Institute of Computer Aided Automation, Computer Vision LabVienna University of TechnologyAustria
  2. 2.Institute of Computer Graphics and Algorithms, Pattern Recongition and Image Processing LabVienna University of TechnologyAustria

Personalised recommendations