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Graph."],"published-print":{"date-parts":[[2012,8,5]]},"abstract":"<jats:p>\n            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\n            <jats:italic>and<\/jats:italic>\n            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\n            <jats:italic>computational geography<\/jats:italic>\n            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.\n          <\/jats:p>","DOI":"10.1145\/2185520.2185597","type":"journal-article","created":{"date-parts":[[2012,8,6]],"date-time":"2012-08-06T18:11:37Z","timestamp":1344276697000},"page":"1-9","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":528,"title":["What makes Paris look like Paris?"],"prefix":"10.1145","volume":"31","author":[{"given":"Carl","family":"Doersch","sequence":"first","affiliation":[{"name":"Carnegie Mellon University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saurabh","family":"Singh","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abhinav","family":"Gupta","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Josef","family":"Sivic","sequence":"additional","affiliation":[{"name":"INRIA\/Ecole Normale Sup\u00e9rieure, Paris"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexei A.","family":"Efros","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University and INRIA\/Ecole Normale Sup\u00e9rieure, Paris"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2012,7]]},"reference":[{"key":"e_1_2_2_1_1","volume-title":"The 2nd Internet Vision Workshop at Conference on Computer Vision and Pattern Recognition (CVPR).","author":"Berg T.","unstructured":"Berg , T. , and Berg , A . 2009. 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Image sequence geolocation with human travel priors. In IEEE 12th International Conference on Computer Vision (ICCV), 253--260."},{"key":"e_1_2_2_13_1","volume-title":"IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1263--1270","author":"Karlinsky L.","unstructured":"Karlinsky , L. , Dinerstein , M. , and Ullman , S . 2009. Unsupervised feature optimization (ufo): Simultaneous selection of multiple features with their detection parameters . In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1263--1270 . Karlinsky, L., Dinerstein, M., and Ullman, S. 2009. Unsupervised feature optimization (ufo): Simultaneous selection of multiple features with their detection parameters. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1263--1270."},{"key":"e_1_2_2_14_1","volume-title":"European Conference on Computer Vision (ECCV), 748--761","author":"Knopp J.","unstructured":"Knopp , J. , Sivic , J. , and Pajdla , T . 2010. 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