What Makes Paris Look like Paris?
Carl Doersch, Saurabh Singh, Abhinav Gupta, Josef Sivic, Alexei A. Efros
ACM Transactions on Graphics / SIGGRAPH (2012)

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.

Carl Doersch, Saurabh Singh, Abhinav Gupta, Josef Sivic, Alexei A. Efros (2012). What Makes Paris Look like Paris?. ACM Transactions on Graphics / SIGGRAPH, 31(4).

title = {What Makes Paris Look like Paris?},
author = {Carl Doersch and Saurabh Singh and Abhinav Gupta and Josef Sivic and Alexei A. Efros},
journal = {ACM Transactions on Graphics / SIGGRAPH},
volume = {31},
number = {4},
year = {2012},
links = "http://graphics.cs.cmu.edu/projects/whatMakesParis/",