The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
What makes Paris look like Paris?
Abstract
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.
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Cites
Histograms of Oriented Gradients for Human Detection
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Metadata
- Authors
- Doersch, C., Singh, S., Gupta, A., Sivic, J., & Efros, A. A.
- Venue
- ACM Transactions on Graphics
- Published
- 2012-07
- Volume
- 31
- Issue
- 4
- Pages
- 1-9
- ISSN
- 0730-0301 (ISSN-L)
- 1557-7368
- OpenAlex
- W2055132753
- Cited by
- 663
- References
- 2
- FWCI
- 46.56
- Type
- journal article
- Language
- en
- Open access
- closed
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