Carnegie Mellon and INRIA/Ecole Normale Supérieure in Paris Develop Visual Data Mining Software used on Google Street View images of Paris, London, New York, Barcelona (VIDEO)
This software that can automatically detect these sometimes subtle features, such as street signs, streetlamps and balcony railings, that give Paris and other cities a distinctive look.
The software analyzed more than 250 million visual elements gleaned from 40,000 Google Street View images of Paris, London, New York, Barcelona and eight other cities to find those that were both frequent and could be used to discriminate one city from the others. This yielded sets of geo-informative visual elements unique to each city, such as cast-iron balconies in Paris, fire escapes in New York City and bay windows in San Francisco.
The discovered visual elements can be useful for a variety of computational geography tasks. Examples include mapping architectural correspondences and influences within and across cities, or finding representative elements at different geo-spatial scales such as a continent, a city, or a specific neighborhood.
Researchers will present their findings Aug. 9 at SIGGRAPH 2012, the International Conference on Computer Graphics and Interactive Techniques, at the Los Angeles Convention Center.