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What Makes Paris Look Like Paris?

Abstract : Given a large repository of geo-tagged imagery, we seek to automatically find visual elements, for example windows, balconies, and street signs, that are most distinctive for a certain geo-spatial area, for example the city of Paris. This is a tremendously difficult task as the visual features distinguishing architectural elements of different places can be very subtle. In addition, we face a hard search problem: given all possible patches in all images, which of them are both frequently occurring and geographically informative? To address these issues, we propose to use a discriminative clustering approach able to take into account the weak geographic supervision. We show that geographically representative image elements can be discovered automatically from Google Street View imagery in a discriminative manner. We demonstrate that these elements are visually interpretable and perceptually geo-informative. The discovered visual elements can also support a variety of computational geography tasks, such as mapping architectural correspondences and influences within and across cities, finding representative elements at different geo-spatial scales, and geographically informed image retrieval.
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Submitted on : Thursday, December 31, 2015 - 12:16:00 AM
Last modification on : Friday, January 21, 2022 - 3:15:58 AM
Long-term archiving on: : Tuesday, April 5, 2016 - 9:47:23 AM


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Carl Doersch, Saurabh Singh, Abhinav Gupta, Josef Sivic, Alexei Efros. What Makes Paris Look Like Paris?. Communications of the ACM, Association for Computing Machinery, 2015, 58 (12), pp.103-110. ⟨10.1145/2830541⟩. ⟨hal-01248528⟩



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