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