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.
Highlights
We presented 11 subjects with 100 random Street View images of which 50% were from Paris, and the rest from eleven other cities
The left column shows randomly chosen images from Google Street View, while the right column shows some of the top-ranked visual element clusters that were automatically discovered
In Paris, the top-scoring elements zero-in on some of the main features that make Paris look like Paris: doors, balconies, windows with railings, street signs and special Parisian lampposts
Summary
We presented 11 subjects with 100 random Street View images of which 50% were from Paris, and the rest from eleven other cities. Subjects were correct 79% of the time (std = 6.3), with chance at 50% (when allowed to scrutinize the text, performance for some subjects went up as high as 90%). What this suggests is that people are remarkably sensitive to the geographically informative features within the visual environment. Finding those features can be difficult though, since every image can contain more than 25,000 candidate patches, and only a tiny fraction will be truly distinctive
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