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Mapping the world's photos

Authors:

David J. Crandall,

Lars Backstrom,

Daniel Huttenlocher,

Jon KleinbergAuthors Info & Claims

WWW '09: Proceedings of the 18th international conference on World wide web

Pages 761 - 770

https://doi.org/10.1145/1526709.1526812

Published: 20 April 2009 Publication History

Abstract

We investigate how to organize a large collection of geotagged photos, working with a dataset of about 35 million images collected from Flickr. Our approach combines content analysis based on text tags and image data with structural analysis based on geospatial data. We use the spatial distribution of where people take photos to define a relational structure between the photos that are taken at popular places. We then study the interplay between this structure and the content, using classification methods for predicting such locations from visual, textual and temporal features of the photos. We find that visual and temporal features improve the ability to estimate the location of a photo, compared to using just textual features. We illustrate using these techniques to organize a large photo collection, while also revealing various interesting properties about popular cities and landmarks at a global scale.

References

[1]

S. Ahern, M. Naaman, R. Nair, J. Yang. World explorer: visualizing aggregate data from unstructured text in geo-referenced collections, JCDL 2007.

Digital Library

[2]

D. Comaniciu, P. Meer. Mean shift: a robust approach toward feature space analysis, PAMI, 24(5), 2002.

Digital Library

[3]

G. Csurka, C. Dance, L. Fan, J. Willamowski, C. Bray. Visual categorization with bags of keypoints. Statistical Learning in Computer Vision, ECCV, 2004.

[4]

http://www.flickr.com/map/

[5]

W. Freeman. The generic viewpoint assumption in a framework for visual perception, Nature, 368(6471), 1994.

[6]

S. Golder, B. Huberman. The Structure of Collaborative Tagging Systems. Journal Information Science, 32(2), 2006.

Digital Library

[7]

S. Golder. Measuring Social Networks with Digital Photograph Collections. ACM Conference on Hypertext and Hypermedia, 2008.

Digital Library

[8]

J. Hays, A. Efros. Scene completion using millions of photographs, SIGGRAPH, 2007.

Digital Library

[9]

J. Hays, A. Efros. IM2GPS: estimating geographic information from a single image, CVPR 2008.

[10]

T. Joachims. Making large-scale SVM learning practical. Advances in kernel methods -- support vector learning, B. Scholkopf et al (ed), MIT-Press, 1999.

Digital Library

[11]

L. Kennedy, M. Naaman. Generating diverse and represent-- ative image search results for landmarks, WWW 2008.

Digital Library

[12]

R. Kumar, J. Novak, A. Tomkins. Structure and Evolution of Online Social Networks. KDD, 2006.

Digital Library

[13]

R. Kumar, P. Raghavan, S. Rajagopalan, A. Tomkins. Trawling the web for emerging cyber-communities, Computer Networks, 1999.

Digital Library

[14]

K. Lerman, L. Jones. Social Browsing on Flickr Intl. Conf. on Weblogs and Social Media, 2007.

[15]

J. Leskovec, L. Backstrom, R. Kumar, A. Tomkins. Microscopic evolution of social networks. KDD, 2008.

Digital Library

[16]

D. Lowe. Distinctive image features from scale- invariant keypoints, Int. J. Computer Vision, 60(2), 2004.

Digital Library

[17]

C. Manning, P. Raghavan, H. Schutze. Introduction to Information Retrieval. Cambridge University Press, 2008.

Digital Library

[18]

C. Marlow, M. Naaman, D. Boyd, M. Davis. Position Paper, Tagging, Taxonomy, Flickr, Article, ToRead, Collaborative Web Tagging Workshop (at WWW), 2006.

[19]

A. Oliva, A. Torralba. Modeling the shape of the scene: a holistic representation of the spatial envelope, Int. J. Computer Vision}, 42(3), 2001.

Digital Library

[20]

S. Sen, S. Lam, A. Mamunur Rashid, D. Cosley, D. Frankowski, J. Osterhouse, F. Maxwell Harper, J. Riedl. Tagging, communities, vocabulary, evolution. CSCW, 2006.

Digital Library

[21]

J. Shi, J. Malik. Normalized cuts and image segmentation, PAMI, 22(8), 2000.

Digital Library

[22]

I. Simon, N. Snavely, S. Seitz. Scene summarization for online image collections, ICCV 2007.

[23]

N. Snavely, S. Seitz, R. Szeliski. Photo tourism: exploring photo collections in 3d, SIGGRAPH, 2006.

Digital Library

[24]

H.T. Welser, E. Gleave, D. Fisher, M. Smith. Visualizing the Signatures of Social Roles in Online Discussion Groups, J. Social Structure, 2007.

Cited By

Li K(2024)Research on the Factors Influencing the Spatial Quality of High-Density Urban Streets: A Framework Using Deep Learning, Street Scene Images, and Principal Component AnalysisLand10.3390/land1308116113:8(1161)Online publication date: 29-Jul-2024
https://doi.org/10.3390/land13081161
Madake JPadwal APande YNevase PBhatlawande S(2024)Advancing Outdoor Landmark Detection: A Vision Transformer Approach2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT61001.2024.10725838(1-6)Online publication date: 24-Jun-2024
https://doi.org/10.1109/ICCCNT61001.2024.10725838
Haas LSkreta MAlberti SFinn C(2024)PIGEON: Predicting Image Geolocations2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.01225(12893-12902)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.01225
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Index Terms

Mapping the world's photos
1. Human-centered computing
  1. Visualization
    1. Visualization application domains
      1. Geographic visualization
2. Information systems
  1. Information systems applications

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Information & Contributors

Information

Published In

cover image ACM Conferences

WWW '09: Proceedings of the 18th international conference on World wide web

April 2009

1280 pages

ISBN:9781605584874

DOI:10.1145/1526709

General Chairs:
Juan Quemada
DIT-UPM
,
Gonzalo León
DIT-UPM
,
Program Chairs:
Yoelle Maarek
Google Inc., Israel
,
Wolfgang Nejdl
L3S and Hannover University

Copyright © 2009 IW3C2 org.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 April 2009

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WWW '09

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WWW '09: The 18th International World Wide Web Conference

April 20 - 24, 2009

Madrid, Spain

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Li K(2024)Research on the Factors Influencing the Spatial Quality of High-Density Urban Streets: A Framework Using Deep Learning, Street Scene Images, and Principal Component AnalysisLand10.3390/land1308116113:8(1161)Online publication date: 29-Jul-2024
https://doi.org/10.3390/land13081161
Madake JPadwal APande YNevase PBhatlawande S(2024)Advancing Outdoor Landmark Detection: A Vision Transformer Approach2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT61001.2024.10725838(1-6)Online publication date: 24-Jun-2024
https://doi.org/10.1109/ICCCNT61001.2024.10725838
Haas LSkreta MAlberti SFinn C(2024)PIGEON: Predicting Image Geolocations2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.01225(12893-12902)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.01225
Fazal NFränti P(2024)Social media data for content creation in location-based gamesJournal of Location Based Services10.1080/17489725.2024.2414000(1-28)Online publication date: 30-Oct-2024
https://doi.org/10.1080/17489725.2024.2414000
Muñoz-Cancino RRíos SGraña M(2024)Enhancing Land Use Patterns Understanding with Multi-Sensor, Multi-Temporal MetricsProcedia Computer Science10.1016/j.procs.2024.09.320246(3198-3207)Online publication date: 2024
https://doi.org/10.1016/j.procs.2024.09.320
Qu HTeh BNordin NLiang Z(2024)Analysis of Guangzhou City Image Perception Based on Weibo Text Data (2019-2023)Heliyon10.1016/j.heliyon.2024.e36577(e36577)Online publication date: Aug-2024
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Son BSon SKim G(2023)Meta Surf: A Metaverse Travel Platform That Connects Real and Virtual Space-Time CoordinatesJournal of Digital Contents Society10.9728/dcs.2023.24.6.122924:6(1229-1241)Online publication date: 30-Jun-2023
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Muñoz-Cancino RRíos SGraña M(2023)Clustering Cities over Features Extracted from Multiple Virtual Sensors Measuring Micro-Level Activity Patterns Allows One to Discriminate Large-Scale City CharacteristicsSensors10.3390/s2311516523:11(5165)Online publication date: 29-May-2023
https://doi.org/10.3390/s23115165
Naggita KLaChance JXiang A(2023)Flickr Africa: Examining Geo-Diversity in Large-Scale, Human-Centric Visual DataProceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society10.1145/3600211.3604659(520-530)Online publication date: 8-Aug-2023
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Soumm MPopescu ADelezoide B(2023)Vis2Rec: A Large-Scale Visual Dataset for Visit Recommendation2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV56688.2023.00300(2986-2996)Online publication date: Jan-2023
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