Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/1291233.1291384acmconferencesArticle/Chapter ViewAbstractPublication PagesmmConference Proceedingsconference-collections
Article

How flickr helps us make sense of the world: context and content in community-contributed media collections

Published: 29 September 2007 Publication History

Abstract

The advent of media-sharing sites like Flickr and YouTube has drastically increased the volume of community-contributed multimedia resources available on the web. These collections have a previously unimagined depth and breadth, and have generated new opportunities - and new challenges - to multimedia research. How do we analyze, understand and extract patterns from these new collections? How can we use these unstructured, unrestricted community contributions of media (and annotation) to generate "knowledge".
As a test case, we study Flickr - a popular photo sharing website. Flickr supports photo, time and location metadata, as well as a light-weight annotation model. We extract information from this dataset using two different approaches. First, we employ a location-driven approach to generate aggregate knowledge in the form of "representative tags" for arbitrary areas in the world. Second, we use a tag-driven approach to automatically extract place and event semantics for Flickr tags, based on each tag's metadata patterns.
With the patterns we extract from tags and metadata, vision algorithms can be employed with greater precision. In particular, we demonstrate a location-tag-vision-based approach to retrieving images of geography-related landmarks and features from the Flickr dataset. The results suggest that community-contributed media and annotation can enhance and improve our access to multimedia resources - and our understanding of the world.

References

[1]
S. Ahern, M. Naaman, R. Nair, and J. Yang. World explorer: Visualizing aggregate data from unstructured text in geo-referenced collections. In Proceedings of the Seventh ACM/IEEE-CS Joint Conference on Digital Libraries. ACM Press, June 2007.
[2]
M. Ames and M. Naaman. Why we tag: Motivations for annotation in mobile and online media. In CHI '07: Proceedings of the SIGCHI conference on Human Factors in computing systems, New York, NY, USA, 2007. ACM Press.
[3]
T. L. Berg and D. A. Forsyth. Automatic ranking of iconic images. Technical report, U. C. Berkeley, January 2007.
[4]
C.-C. Chang and C.-J. Lin. LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/öcjlin/libsvm.
[5]
M. Davis, M. Smith, F. Stentiford, A. Bambidele, J. Canny, N. Good, S. King, and R. Janakiraman. Using context and similarity for face and location identification. In Proceedings of the IS&T/SPIE 18th Annual Symposium on Electronic Imaging Science and Technology, 2006.
[6]
M. Dubinko, R. Kumar, J. Magnani, J. Novak, P. Raghavan, and A. Tomkins. Visualizing tags over time. In WWW '06: Proceedings of the 15th international conference on World Wide Web, pages 193--202, New York, NY, USA, 2006. ACM Press.
[7]
R. Fergus, P. Perona, and A. Zisserman. A visual category filter for Google images. Proc. ECCV, pages 242--256, 2004.
[8]
W. Hsu, L. Kennedy, and S.-F. Chang. Video search reranking via information bottleneck principle. In ACM Multimedia, Santa Babara, CA, USA, 2006.
[9]
A. Jaffe, M. Naaman, T. Tassa, and M. Davis. Generating summaries and visualization for large collections of geo-referenced photographs. In MIR '06: Proceedings of the 8th ACM international workshop on Multimedia information retrieval, pages 89--98, New York, NY, USA, 2006. ACM Press.
[10]
L. Kennedy and S.-F. Chang. A reranking approach for context-based concept fusion in video indexing and retrieval. In Conference on Image and Video Retrieval, Amsterdam, 2007.
[11]
L. Kennedy, S.-F. Chang, and I. Kozintsev. To search or to label?: predicting the performance of search-based automatic image classifiers. Proceedings of the 8th ACM international workshop on Multimedia information retrieval, pages 249--258, 2006.
[12]
D. Lowe. Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision, 60(2):91--110, 2004.
[13]
B. Manjunath and W. Ma. Texture features for browsing and retrieval of image data. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 18(8):837--842, 1996.
[14]
M. Naaman, A. Paepcke, and H. Garcia-Molina. From where to what: Metadata sharing for digital photographs with geographic coordinates. In 10th International Conference on Cooperative Information Systems (CoopIS), 2003.
[15]
M. Naaman, Y. J. Song, A. Paepcke, and H. Garcia-Molina. Automatic organization for digital photographs with geographic coordinates. In Proceedings of the Fourth ACM/IEEE-CS Joint Conference on Digital Libraries, 2004.
[16]
A. Natsev, M. Naphade, and J. Tešić. Learning the semantics of multimedia queries and concepts from a small number of examples. Proceedings of the 13th annual ACM international conference on Multimedia, pages 598--607, 2005.
[17]
N. O'Hare, C. Gurrin, G. J. Jones, and A. F. Smeaton. Combination of content analysis and context features for digital photograph retrieval. In 2nd IEE European Workshop on the Integration of Knowledge, Semantic and Digital Media Technologies, 2005.
[18]
A. Pigeau and M. Gelgon. Organizing a personal image collection with statistical model-based ICL clustering on spatio-temporal camera phone meta-data. Journal of Visual Communication and Image Representation, 15(3):425--445, September 2004.
[19]
T. Rattenbury, N. Good, and M. Naaman. Towards automatic extraction of event and place semantics from flickr tags. In Proceedings of the Thirtieth International ACM SIGIR Conference. ACM Press, July 2007.
[20]
R. Sarvas, E. Herrarte, A. Wilhelm, and M. Davis. Metadata creation system for mobile images. In Proceedings of the 2nd international conference on Mobile systems, applications, and services, pages 36--48. ACM Press, 2004.
[21]
A. W. M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain. Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Anal. Mach. Intell., 22(12):1349--1380, 2000.
[22]
N. Snavely, S. Seitz, and R. Szeliski. Photo tourism: exploring photo collections in 3D. ACM Transactions on Graphics (TOG), 25(3):835--846, 2006.
[23]
M. Stricker and M. Orengo. Similarity of color images. Proc. SPIE Storage and Retrieval for Image and Video Databases, 2420:381--392, 1995.
[24]
K. Toyama, R. Logan, and A. Roseway. Geographic location tags on digital images. In Proceedings of the 11th International Conference on Multimedia (MM2003), pages 156--166. ACM Press, 2003.
[25]
C.-M. Tsai, A. Qamra, and E. Chang. Extent: Inferring image metadata from context and content. In IEEE International Conference on Multimedia and Expo. IEEE, 2005.
[26]
V. Vapnik. The Nature of Statistical Learning Theory. Springer, 2000.
[27]
Y. Wu, E. Y. Chang, and B. L. Tseng. Multimodal metadata fusion using causal strength. In Proceedings of the 13th International Conference on Multimedia (MM2005), pages 872--881, New York, NY, USA, 2005. ACM Press.

Cited By

View all
  • (2024)Advanced Multimodal Deep Learning Architecture for Image-Text Matching2024 IEEE 4th International Conference on Electronic Technology, Communication and Information (ICETCI)10.1109/ICETCI61221.2024.10594167(1185-1191)Online publication date: 24-May-2024
  • (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
  • (2024)Relevant Tag Extraction Based on Image Visual ContentApplied Intelligence10.1007/978-981-97-0827-7_25(283-295)Online publication date: 1-Mar-2024
  • Show More Cited By

Index Terms

  1. How flickr helps us make sense of the world: context and content in community-contributed media collections

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    MM '07: Proceedings of the 15th ACM international conference on Multimedia
    September 2007
    1115 pages
    ISBN:9781595937025
    DOI:10.1145/1291233
    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]

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 29 September 2007

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. geo-referenced photographs
    2. photo collections
    3. social media

    Qualifiers

    • Article

    Conference

    MM07

    Acceptance Rates

    Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)40
    • Downloads (Last 6 weeks)4
    Reflects downloads up to 08 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2024)Advanced Multimodal Deep Learning Architecture for Image-Text Matching2024 IEEE 4th International Conference on Electronic Technology, Communication and Information (ICETCI)10.1109/ICETCI61221.2024.10594167(1185-1191)Online publication date: 24-May-2024
    • (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
    • (2024)Relevant Tag Extraction Based on Image Visual ContentApplied Intelligence10.1007/978-981-97-0827-7_25(283-295)Online publication date: 1-Mar-2024
    • (2024)A Location Recommendation Model Based on User Behavior and Sequence InfluenceInternet of Things – ICIOT 202310.1007/978-3-031-51734-1_2(18-30)Online publication date: 19-Jan-2024
    • (2023)An Urban Image Stimulus Set Generated from Social MediaData10.3390/data81201848:12(184)Online publication date: 1-Dec-2023
    • (2023)Vocabulary in urban contexts using multimodalityWorking papers in Applied Linguistics and Linguistics at York10.25071/2564-2855.283Online publication date: 6-Nov-2023
    • (2023)Identifying Hashtag Cultures to Study the Construction of Childhood Image and Parents’ AspirationsCulture & Psychology10.1177/1354067X231191480Online publication date: 28-Jul-2023
    • (2023)Extracting Place Functionality From Crowdsourced Textual Data Using Semantic Space ModelingIEEE Access10.1109/ACCESS.2023.333285411(129217-129229)Online publication date: 2023
    • (2023)Researching heritage values in social media environments: understanding variabilities and (in)visibilitiesInternational Journal of Heritage Studies10.1080/13527258.2023.223191929:10(1021-1040)Online publication date: 6-Jul-2023
    • (2022)Clustering Method for Touristic Photographic Spots RecommendationAdvanced Data Mining and Applications10.1007/978-3-031-22137-8_17(223-237)Online publication date: 24-Nov-2022
    • Show More Cited By

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media