Linking Past to Present: Discovering Style in Two Centuries of Architecture

Stefan Lee 1 Nicolas Maisonneuve 2, 3 David Crandall 1 Alexei A. Efros 4 Josef Sivic 2, 3
2 WILLOW - Models of visual object recognition and scene understanding
DI-ENS - Département d'informatique de l'École normale supérieure, Inria Paris-Rocquencourt, CNRS - Centre National de la Recherche Scientifique : UMR8548
Abstract : With vast quantities of imagery now available online, researchers have begun to explore whether visual patterns can be discovered automatically. Here we consider the particular domain of architecture, using huge collections of street-level imagery to find visual patterns that correspond to semantic-level architectural elements distinctive to particular time periods. We use this analysis both to date buildings, as well as to discover how functionally-similar architectural elements (e.g. windows, doors, balconies , etc.) have changed over time due to evolving styles. We validate the methods by combining a large dataset of nearly 150,000 Google Street View images from Paris with a cadastre map to infer approximate construction date for each facade. Not only could our analysis be used for dating or geo-localizing buildings based on architectural features, but it also could give architects and historians new tools for confirming known theories or even discovering new ones.
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Communication dans un congrès
IEEE International Conference on Computational Photography, Apr 2015, Houston, United States
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Stefan Lee, Nicolas Maisonneuve, David Crandall, Alexei A. Efros, Josef Sivic. Linking Past to Present: Discovering Style in Two Centuries of Architecture. IEEE International Conference on Computational Photography, Apr 2015, Houston, United States. 〈hal-01152482〉

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