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Journal Articles ACM Transactions on Graphics Year : 2012

What Makes Paris Look like Paris?

Abstract

Given a large repository of geotagged imagery, we seek to automatically find visual elements, e.g. 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 dis- covered 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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Dates and versions

hal-01053876 , version 1 (03-08-2014)

Identifiers

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Carl Doersch, Saurabh Singh, Abhinav Gupta, Josef Sivic, Alexei Efros. What Makes Paris Look like Paris?. ACM Transactions on Graphics, 2012, 31 (4), ⟨10.1145/2830541⟩. ⟨hal-01053876⟩
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