Architectural Style Classification of Domes

  • Gayane Shalunts
  • Yll Haxhimusa
  • Robert Sablatnig
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7432)


Domes are architectural structural elements characteristic for ecclesiastical and secular monumental buildings, like churches, basilicas, mosques, capitols and city halls. In the scope of building facade architectural style classification the current paper addresses the problem of architectural style classification of facade domes. Building facade classification by architectural styles is achieved by classification and voting of separate architectural elements, like domes, windows, towers, etc. Typical forms of the structural elements bear the signature of each architectural style. Our approach classifies domes of three architectural styles - Renaissance, Russian and Islamic. We present a three-step approach, which in the first step analyzes the height and width of the dome for the identification of Islamic saucer domes, in the second step detects golden color in YCbCr color space to determine Russian golden onion domes and in the third step performs classification based on dome shapes, using clustering and learning of local features. Thus we combine three features - the relation of dome width and height, color and shape, in a single methodology to achieve high classification rate.


Interest Point Query Image Scale Invariant Feature Transform Architectural Style Architectural Element 
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.


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  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: Proc. of ICCV and PR, 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: Proc. of IEEE 12th ICCV, 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. IJCV 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.
    Shalunts, G., Haxhimusa, Y., Sablatnig, R.: Architectural Style Classification of Building Facade Windows. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Wang, S., Kyungnam, K., Benes, B., Moreland, K., Borst, C., DiVerdi, S., Yi-Jen, C., Ming, J. (eds.) ISVC 2011, Part II. LNCS, vol. 6939, pp. 280–289. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  7. 7.
    Shalunts, G., Haxhimusa, Y., Sablatnig, R.: Classification of gothic and baroque architectural elements. In: Proc. of the 19th IWSSIP, Vienna, Austria, pp. 330–333 (2012)Google Scholar
  8. 8.
    Mathias, M., Martinovic, A., Weissenberg, J., Haegler, S., Gool, L.V.: Automatic architectural style recognition. In: Proc. of the 4th International Workshop on 3D Virtual Reconstruction and Visualization of Complex Architectures. International Society for Photogrammetry and Remote Sensing, Trento (2011)Google Scholar
  9. 9.
    Rosengarten, A.: A handbook of architectural styles. Chatto and Windus, London (1912)Google Scholar
  10. 10.
    Basilio, J.A.M., Torres, G.A., Pérez, G.S., Medina, L.K.T., Meana, H.M.P.: Explicit image detection using ycbcr space color model as skin detection. In: Proc. of the 2011 American Conference on Applied Mathematics and the 5th WSEAS International Conference on Computer Engineering and Applications, pp. 123–128 (2011)Google Scholar
  11. 11.
    Maglogiannis, I., Vouyioukas, D., Aggelopoulos, C.: Face detection and recognition of natural human emotion using markov random fields. Personal and Ubiquitous Computing 13(1), 95–101 (2009)CrossRefGoogle Scholar
  12. 12.
    Ojala, T., Pietikäinen, 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
  13. 13.
    Haralick, R.M.: Statistical and structural approaches to texture. Proc. IEEE 67, 786–804 (1979)CrossRefGoogle Scholar
  14. 14.
    Zhang, D., Lu, G.: Review of shape representation and description techniques. Pattern Recognition 37, 1–19 (2004)zbMATHCrossRefGoogle Scholar
  15. 15.
    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
  16. 16.
    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
  17. 17.
    Harris, C., Stephens, M.: A combined corner and edge detector. In: Proc. of the 4th Alvey Vision Conference, pp. 147–151 (1998)Google Scholar
  18. 18.
    Mikolajczyk, K., Schmid, C.: Indexing based on scale invariant interest points. In: Internationl Conference in Computer Vision, pp. 525–531 (2001)Google Scholar
  19. 19.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. IJCV 60(2), 91–110 (2004)CrossRefGoogle Scholar
  20. 20.
    Matas, J., Chum, O., Urban, M., Pajdla1, T.: Robust wide baseline stereo from maximally stable extremal regions. In: BMVC, pp. 384–393 (2002)Google Scholar
  21. 21.
    Tuytelaars, T., Gool, L.V.: Wide baseline stereo matching based on local, affinely invariant regions. In: BMVC, pp. 412–425 (2000)Google Scholar
  22. 22.
    Csurka, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Workshop on Statistical Learning in Computer Vision, ECCV, pp. 1–22 (2004)Google Scholar
  23. 23.
    Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55(1), 119–139 (1997)MathSciNetzbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

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

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