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How Flickr Helps us Make Sense of the World
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How Flickr Helps us Make Sense of the World

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ACM Multimedia 2007 presentation: How Flickr Helps us Make Sense of the World: Context and Content in Community-Contributed Media Collections.

ACM Multimedia 2007 presentation: How Flickr Helps us Make Sense of the World: Context and Content in Community-Contributed Media Collections.

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  • Lyndon is phd intern from Columbia Tye phd intern from Berkeley But really, I will talk about some research challenges and potential in community-generated collections of media, and how we begin to address those.
  • Transcript

    • 1. How Flickr Helps us Make Sense of the World: Context and Content in Community-Contributed Media Collections Lyndon Kennedy Mor Naaman* Share Ahern Rahul Nair Tye Rattenbury Yahoo! Research Berkeley Yahoo! Advanced Development Research *me
    • 2. Community-contributed?
      • Media
      • Descriptive text (title, caption, tag)
      • Discussions and comments
      • Views and view patterns
      • Item use and feedback
      • Reuse and remix
      • Micro- and explicit recommendations
      • “ Context Metadata”
    • 3. Research Challenges
      • Content is still hard…
      • Unstructured data (no semantics)
      • Noise
      • Scale
        • Computation
        • Long tail implies no supervised learning
      • Bias/feedback/Spam
    • 4. Foremost Challenge:
      • What’s the user problem?
        • Navigation/exploration
        • Recommendation
        • New application
        • Other?
      • Grounded in real needs
      • What impact on the
      • community?
      “ Social Media Cycle”
    • 5. In Particular…
      • No tigers, beaches and sunsets. Please.
    • 6. Flickr Tigers
    • 7. Good news! Patterns That Make Sense:
      • Semantic space
      • Activity and viewing data
      • User/personal data
      • Social network
      • And, location/time:
    • 8. Data Description
    • 9. That Noise….
      • Noisy data
      • Photographer biases
      • Wrong data
      6 kms 5 kms
    • 10. Tag Patterns
    • 11. Tag Patterns
    • 12. Tag Patterns
    • 13. Experiments byobw We can derive tag semantics using location and time metadata. Also see [Rattenbury et al, SIGIR 2007]
    • 14. Can We Create Useful Applications?
        • Flickr “geotagged” in San Francisco
    • 15. Intuition
        • More “activity” in a certain location indicates importance of that location
        • Tag that are unique to a certain location can represent the location better
    • 16. Tag Maps - SF
    • 17. Make a World Explorer http://tagmaps.research.yahoo.com Also see [Ahern et al., JCDL 2007]
    • 18. Rolling in Content
      • So far, we leveraged metadata patterns to find
        • What are the geo-driven features
        • Where people take photos of these features
      • Can we utilized content analysis?
      • Hmmm….
    • 19. Handling scale
      • Reduce computation requirements
        • Filter using metadata
      • Unsupervised methods
        • Effective for long tail without training
    • 20. Problem: Better Visual Summaries Locations and Landmarks Raw Data Visual Summary?
    • 21. The Problem, in Short and more of this… … without explicitly knowing the difference. Find less of this…
    • 22. Location can help Enough visual similarity for learning?
    • 23. Finding Representative Photos
    • 24. Visual Features
      • Color : moments over a 5x5 grid
      • Texture : Gabor over global image
      • Interest points : SIFT
    • 25. Ranking images: point-wise links Form links between images via matching SIFT points. Rank by degree of connectivity.
    • 26. Landmark Graph Structure More connected Less connected
    • 27. Results: Palace of Fine Arts Tags-only Tags+Location Tags+Location+Visual X X X X X X X
    • 28. Initial Evaluation
      • Select 10 landmarks to evaluate
      • Identify landmarks region(s) of relevance
      • Apply visual approach to discover representative images
      • Evaluate using Precision @ 10
    • 29. Performance Average +45% from visual +30% from location
    • 30. Evaluation Issues
      • Degrees of “Representativeness”
    • 31. Evaluation Issues
      • Diversity of Results
    • 32. Conclusions
      • Noise can be handled (sometimes)
      • Can generate some structure from the unstructured
      • Content can help with the right tasks
      • Bias and Spam?
    • 33. Thanks
      • With: Lyndon Kennedy, Shane Ahern, Rahul Nair, Tye Rattenbury
      • Jeannie Yang, Nathan Good, Simon King
      • In the papers: MIR06, JCDL07, SIGIR07
      • Have a Nokia phone? Check out ZoneTag and Zurfer
      • Read more, follow: http://www.whyrb.com
      • Slides: http://slideshare.net/mor
      • Mor Naaman: mor@yahoo-inc.com
    • 34. APIs for all!
      • Everything we can do, you can do (better). APIs include:
        • Cell Tower ID database
        • Suggested Tags
        • TagMaps data
        • TagMaps Widget
        • ZoneTag RSS feeds, Action Tags
      http://developer.yahoo.com/yrb/
    • 35. Tag Maps - Paris

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