An emerging field of study in the realm of urban issues has been that of food source planning. Not only are areas becoming more focused on local, sustainable food, but there is a growing movement to educate people in urban areas (who are typically very detached from the process of which food is grown, harvested, […]
Some really cool stuff is happening over at Carnegie Mellon University.
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.
Hats off to Carl Doersch, Saurabh Singh, Abhinav Gupta, Josef Sivic and Alexei A. Efros for their work on this project. For more info, check out the project website. There’s even a full download of their report. Image courtesy of Jazzbery Blue, who has lots of cool stuff for sale here.
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