Abstract
Architectural style classification differs from standard classification tasks due to the rich inter-class relationships between different styles, such as re-interpretation, revival, and territoriality. In this paper, we adopt Deformable Part-based Models (DPM) to capture the morphological characteristics of basic architectural components and propose Multinomial Latent Logistic Regression (MLLR) that introduces the probabilistic analysis and tackles the multi-class problem in latent variable models. Due to the lack of publicly available datasets, we release a new large-scale architectural style dataset containing twenty-five classes. Experimentation on this dataset shows that MLLR in combination with standard global image features, obtains the best classification results. We also present interpretable probabilistic explanations for the results, such as the styles of individual buildings and a style relationship network, to illustrate inter-class relationships.
Chapter PDF
Similar content being viewed by others
References
Berg, A.C., Grabler, F., Malik, J.: Parsing images of architectural scenes. In: IEEE 11th International Conference on Computer Vision, ICCV 2007, pp. 1–8. IEEE (2007)
Borgatti, S.: Netdraw software for network visualization. Analytic Technologies (2002)
Chu, W.T., Tsai, M.H.: Visual pattern discovery for architecture image classification and product image search. In: Proceedings of the 2nd ACM International Conference on Multimedia Retrieval, p. 27. ACM (2012)
Doersch, C., Singh, S., Gupta, A., Sivic, J., Efros, A.A.: What makes paris look like paris? ACM Transactions on Graphics (TOG) 31(4), 101 (2012)
Dunlop, C.: Architectural Styles. Dearborn Real Estate (2003)
Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(9), 1627–1645 (2010)
Freeman, W.T., Tenenbaum, J.B.: Learning bilinear models for two-factor problems in vision. In: Proceedings of the 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 554–560. IEEE (1997)
Goel, A., Juneja, M., Jawahar, C.: Are buildings only instances?: exploration in architectural style categories. In: Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing, p. 1. ACM (2012)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 2169–2178 (2006)
Lee, Y.J., Efros, A.A., Hebert, M.: Style-aware mid-level representation for discovering visual connections in space and time. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 1857–1864. IEEE (2013)
Li, L.J., Su, H., Fei-Fei, L., Xing, E.P.: Object bank: A high-level image representation for scene classification & semantic feature sparsification. In: Advances in Neural Information Processing Systems, pp. 1378–1386 (2010)
Lin, H.T., Lin, C.J., Weng, R.C.: A note on platts probabilistic outputs for support vector machines. Machine Learning 68(3), 267–276 (2007)
Pandey, M., Lazebnik, S.: Scene recognition and weakly supervised object localization with deformable part-based models. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 1307–1314. IEEE (2011)
Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: Object retrieval with large vocabularies and fast spatial matching. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–8 (June 2007)
Shrivastava, A., Malisiewicz, T., Gupta, A., Efros, A.A.: Data-driven visual similarity for cross-domain image matching. ACM Transactions on Graphics (TOG) 30, 154 (2011)
Torralba, A., Murphy, K.P., Freeman, W.T., Rubin, M.A.: Context-based vision system for place and object recognition. In: Proceedings of the Ninth IEEE International Conference on Computer Vision, pp. 273–280. IEEE (2003)
Vondrick, C., Khosla, A., Malisiewicz, T., Torralba, A.: Hoggles: Visualizing object detection features. In: ICCV (2013)
Watanabe, S.: Discrimination of painting style and quality: pigeons use different strategies for different tasks. Animal Cognition 14(6), 797–808 (2011)
Wu, G., Chang, E.Y.: Class-boundary alignment for imbalanced dataset learning. In: ICML 2003 Workshop on Learning from Imbalanced Data Sets II, Washington, DC, pp. 49–56 (2003)
Zhang, L., Song, M., Liu, X., Sun, L., Chen, C., Bu, J.: Recognizing architecture styles by hierarchical sparse coding of blocklets. Information Sciences 254, 141–154 (2014)
Zujovic, J., Gandy, L., Friedman, S., Pardo, B., Pappas, T.N.: Classifying paintings by artistic genre: An analysis of features & classifiers. In: IEEE International Workshop on Multimedia Signal Processing, MMSP 2009, pp. 1–5. IEEE (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
1 Electronic Supplementary Material
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Xu, Z., Tao, D., Zhang, Y., Wu, J., Tsoi, A.C. (2014). Architectural Style Classification Using Multinomial Latent Logistic Regression. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds) Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8689. Springer, Cham. https://doi.org/10.1007/978-3-319-10590-1_39
Download citation
DOI: https://doi.org/10.1007/978-3-319-10590-1_39
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10589-5
Online ISBN: 978-3-319-10590-1
eBook Packages: Computer ScienceComputer Science (R0)