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.
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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)
Zhang, W., Kosecka, J.: Hierarchical building recognition. Image and Vision Computing 25(5), 704–716 (2004)
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)
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)
Snavely, N., Seitz, S.M., Szeliski, R.: Photo tourism: exploring photo collections in 3d. ACM Transaction on Graphics 25, 835–846 (2006)
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)
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)
Recky, M., Leberl, F.: Window detection in complex facades. In: European Workshop on Visual Information Processing (EUVIP 2010), pp. 220–225 (2010)
Collins, P.: Changing Ideals in Modern Architecture, pp. 1750–1950. McGill-Queen’s University Press (1998)
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)
Haralick, R.M.: Statistical and structural approaches to texture. Proc. IEEE 67, 786–804 (1979)
Zhang, D., Lu, G.: Review of shape representation and description techniques. Pattern Recognition 37, 1–19 (2004)
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)
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)
Harris, C., Stephens, M.: A combined corner and edge detector. In: Proceedings of The Fourth Alvey Vision Conference, pp. 147–151 (1998)
Mikolajczyk, K., Schmid, C.: Indexing based on scale invariant interest points. In: Internationl Conference in Computer Vision, pp. 525–531 (2001)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
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)
Tuytelaars, T., Gool, L.V.: Wide baseline stereo matching based on local, affinely invariant regions. In: British Machine Vision Conference, pp. 412–425 (2000)
Csurka, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: ECCV 2004, pp. 1–22 (2004)
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Shalunts, G., Haxhimusa, Y., Sablatnig, R. (2011). Architectural Style Classification of Building Facade Windows. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2011. Lecture Notes in Computer Science, vol 6939. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24031-7_28
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DOI: https://doi.org/10.1007/978-3-642-24031-7_28
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