What Makes Paris Look Like Paris?

Carl Doersch 1 Saurabh Singh 1 Abhinav Gupta 1 Josef Sivic 2, 3 Alexei Efros 4
3 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 : 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.
Type de document :
Article dans une revue
Communications of the ACM, ACM, 2015, 58 (12), pp.103-110. 〈10.1145/2830541〉
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Soumis le : jeudi 31 décembre 2015 - 00:16:00
Dernière modification le : vendredi 25 mai 2018 - 12:02:06
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Carl Doersch, Saurabh Singh, Abhinav Gupta, Josef Sivic, Alexei Efros. What Makes Paris Look Like Paris?. Communications of the ACM, ACM, 2015, 58 (12), pp.103-110. 〈10.1145/2830541〉. 〈hal-01248528〉



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