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What Makes Paris Look like Paris?

ACM transactions on graphics, 2015-12, Vol.31 (4) [Peer Reviewed Journal]

Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 0730-0301 ;EISSN: 1557-7368 ;DOI: 10.1145/2830541

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  • Title:
    What Makes Paris Look like Paris?
  • Author: Doersch, Carl ; Singh, Saurabh ; Gupta, Abhinav ; Sivic, Josef ; Efros, Alexei
  • Subjects: Computer Science ; Computer Vision and Pattern Recognition
  • Is Part Of: ACM transactions on graphics, 2015-12, Vol.31 (4)
  • Description: 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 dis- covered 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.
  • Publisher: Association for Computing Machinery
  • Language: English
  • Identifier: ISSN: 0730-0301
    EISSN: 1557-7368
    DOI: 10.1145/2830541
  • Source: Hyper Article en Ligne (HAL) (Open Access)
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