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How Flickr Helps us Make Sense of the World
 

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.

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

  • 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
  • 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”
  • Research Challenges
    • Content is still hard…
    • Unstructured data (no semantics)
    • Noise
    • Scale
      • Computation
      • Long tail implies no supervised learning
    • Bias/feedback/Spam
  • 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”
  • In Particular…
    • No tigers, beaches and sunsets. Please.
  • Flickr Tigers
  • Good news! Patterns That Make Sense:
    • Semantic space
    • Activity and viewing data
    • User/personal data
    • Social network
    • And, location/time:
  • Data Description
  • That Noise….
    • Noisy data
    • Photographer biases
    • Wrong data
    6 kms 5 kms
  • Tag Patterns
  • Tag Patterns
  • Tag Patterns
  • Experiments byobw We can derive tag semantics using location and time metadata. Also see [Rattenbury et al, SIGIR 2007]
  • Can We Create Useful Applications?
      • Flickr “geotagged” in San Francisco
  • 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
  • Tag Maps - SF
  • Make a World Explorer http://tagmaps.research.yahoo.com Also see [Ahern et al., JCDL 2007]
  • 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….
  • Handling scale
    • Reduce computation requirements
      • Filter using metadata
    • Unsupervised methods
      • Effective for long tail without training
  • Problem: Better Visual Summaries Locations and Landmarks Raw Data Visual Summary?
  • The Problem, in Short and more of this… … without explicitly knowing the difference. Find less of this…
  • Location can help Enough visual similarity for learning?
  • Finding Representative Photos
  • Visual Features
    • Color : moments over a 5x5 grid
    • Texture : Gabor over global image
    • Interest points : SIFT
  • Ranking images: point-wise links Form links between images via matching SIFT points. Rank by degree of connectivity.
  • Landmark Graph Structure More connected Less connected
  • Results: Palace of Fine Arts Tags-only Tags+Location Tags+Location+Visual X X X X X X X
  • Initial Evaluation
    • Select 10 landmarks to evaluate
    • Identify landmarks region(s) of relevance
    • Apply visual approach to discover representative images
    • Evaluate using Precision @ 10
  • Performance Average +45% from visual +30% from location
  • Evaluation Issues
    • Degrees of “Representativeness”
  • Evaluation Issues
    • Diversity of Results
  • Conclusions
    • Noise can be handled (sometimes)
    • Can generate some structure from the unstructured
    • Content can help with the right tasks
    • Bias and Spam?
  • 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
  • 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/
  • Tag Maps - Paris