skip to main content
10.1145/2390790.2390794acmconferencesArticle/Chapter ViewAbstractPublication PagesmmConference Proceedingsconference-collections
research-article

Exploring Geotagged images for land-use classification

Published:29 October 2012Publication History

ABSTRACT

On-line photo sharing websites such as Flickr not only allow users to share their precious memories with others, they also act as a repository of all kinds of information carried by their photos and tags. In this work, we investigate the problem of geographic discovery, particularly land-use classification, through crowdsourcing of geographic information from Flickr's geotagged photo collections. Our results show that the visual information contained in these photo collections enables us to classify three types of land-use classes on two university campuses. We also show that text entries accompanying these photos are informative for geographic discovery.

References

  1. Flickr photo sharing. http://www.flickr.com.Google ScholarGoogle Scholar
  2. Picasa web albums. http://picasa.google.com/.Google ScholarGoogle Scholar
  3. C.-C. Chang and C.-J. Lin. LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. D. Crandall, L. Backstrom, D. Huttenlocher, and J. Kleinberg. Mapping the world's photos. In Proceedings of the International World Wide Web Conference, pages 761--770, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. M. Cristani, A. Perina, U. Castellani, and V. Murino. Geo-located image analysis using latent representations. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1--8, 2008.Google ScholarGoogle ScholarCross RefCross Ref
  6. T. Hofmann. Probabilistic latent semantic indexing. In SIGIR '99: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 50--57, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. T. Hofmann. Unsupervised learning by probabilistic latent semantic analysis. Machine Learning, 42(1--2):177--196, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. N. Jacobs, S. Satkin, N. Roman, R. Speyer, and R. Pless. Geolocating static cameras. In Proceedings of the IEEE International Conference on Computer Vision, pages 1--6, 2007.Google ScholarGoogle ScholarCross RefCross Ref
  9. Y.-G. Jiang, C.-W. Ngo, and J. Yang. Towards optimal bag-of-features for object categorization and semantic video retrieval. In Proceedings of ACM International Conference on Image and Video Retrieval, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. D. Leung and S. Newsam. Proximate sensing using georeferenced community contributed photo collections. In Procedings of the ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems: Workshop on Location Based Social Networks, pages 57--64, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. D. Leung and S. Newsam. Proximate sensing: Inferring what-is-where from georeferenced photo collections. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2955--2962, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  12. K. Yanai, K. Yaegashi, and B. Qiu. Detecting cultural differences using consumer-generated geotagged photos. In Proceedings of the International Workshop on Location and the Web, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Exploring Geotagged images for land-use classification

          Recommendations

          Comments

          Login options

          Check if you have access through your login credentials or your institution to get full access on this article.

          Sign in
          • Published in

            cover image ACM Conferences
            GeoMM '12: Proceedings of the ACM multimedia 2012 workshop on Geotagging and its applications in multimedia
            October 2012
            46 pages
            ISBN:9781450315906
            DOI:10.1145/2390790

            Copyright © 2012 ACM

            Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

            Publisher

            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 29 October 2012

            Permissions

            Request permissions about this article.

            Request Permissions

            Check for updates

            Qualifiers

            • research-article

            Acceptance Rates

            Overall Acceptance Rate10of14submissions,71%

            Upcoming Conference

            MM '24
            MM '24: The 32nd ACM International Conference on Multimedia
            October 28 - November 1, 2024
            Melbourne , VIC , Australia

          PDF Format

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader