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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Nov 23, 2015
- Source ID
- 10.1145/2830541
Entities
People
- Abhinav Gupta
- Alexei A. Efros
- Carl Doersch
- Josef Sivic
- Saurabh Singh
Organizations
- Carnegie Mellon University
- National Science Foundation
- Office of Naval Research
- University of California, Berkeley
- University of Illinois Urbana–Champaign
- École Normale Supérieure