What makes Paris look like Paris?

  • Authors:

  • Carl Doersch;Saurabh Singh;Abhinav Gupta;Josef Sivic;Alexei A. Efros

  • Affiliations:

  • Carnegie Mellon University;Carnegie Mellon University;Carnegie Mellon University;INRIA/Ecole Normale Supérieure, Paris;Carnegie Mellon University and INRIA/Ecole Normale Supérieure, Paris

  • Venue:
  • ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
  • Year:
  • 2012

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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 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.