What Makes Paris Look Like Paris?
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 disti...
| Publicado en: | Communications of the ACM Vol. 58; no. 12; pp. 103 - 111 |
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| Autores principales: | , , , , |
| Formato: | Artículo |
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Association for Computing Machinery
Dec2015
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=111185923&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 111185923 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00010782 ACM jtl: Communications of the ACM issn: 00010782 maglogo: N pubinfo: dt: Dec2015 vid: 58 iid: 12 pid: 68 pub: Association for Computing Machinery artinfo: ui: 111185923 10.1145/2830541 ppf: 103 ppct: 8 formats: tig: atl: What Makes Paris Look Like Paris? aug: au: Doersch, Carl Singh, Saurabh Gupta, Abhinav Sivic, Josef Efros, Alexei A. affil: Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA. Computer Science Department, University of Illinois, Urbana-Champaign, Champaign, IL. Robotics Institute, Carnegie Mellon University, Pittsburgh, PA. Computer Science Department, INRIA/Ecole Normale Supérieure, Paris, France. Electrical Engineering and Computer Science (EECS) Department, University of California, Berkeley, Berkeley, CA. su: Image processing software Image recognition (Computer vision) Architectural details Geography software Paris (France) description & travel sug: subj: Image processing software Image recognition (Computer vision) Architectural details Geography software Paris (France) description & travel ab: 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. pubtype: Periodical doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2015 holdings: @attributes: islocal: N |
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