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What makes Paris looklike Paris? Carl DoerschSaurabh SinghAbhinav Gupta2012 год

What makes Paris look like Paris?
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Аннотация: 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.
Год издания: 2012
Авторы: Carl Doersch, Saurabh Singh, Abhinav Gupta, Josef Šivic, Alexei A. Efros
Издательство: Association for Computing Machinery
Источник: ACM Transactions on Graphics
Ключевые слова: Remote-Sensing Image Classification, Advanced Image and Video Retrieval Techniques, Automated Road and Building Extraction
Другие ссылки: ACM Transactions on Graphics (HTML)
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