What makes Paris look like Paris?

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 ...

• Type: Journal
• Year: 2012
• Where: TOG
• Authors: Carl Doersch, Saurabh Singh, Abhinav Gupta, Josef Sivic, Alexei A. Efros
• Keywords: Communications, Computational Geography, Computer Vision Image, CR Categories, TOG 2012