Uploaded on

Generating Summaries and Visualization for Large Collections of Geo-referenced Photographs

Generating Summaries and Visualization for Large Collections of Geo-referenced Photographs

More in: Travel , Business
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
  • I saw that. Cool.
    Are you sure you want to
    Your message goes here
  • Update: Check out the live version!
    http://tagmaps.research.yahoo.com
    Are you sure you want to
    Your message goes here
  • It's an internal Y! font, I removed them from the rest of this presentation but some remains were left... so, I don't think it's a problem :)
    Are you sure you want to
    Your message goes here
  • Dude, this slide got hosed!
    Sorry, looks like it's a font problem. We're looking into it...
    Are you sure you want to
    Your message goes here
No Downloads

Views

Total Views
4,022
On Slideshare
0
From Embeds
0
Number of Embeds
0

Actions

Shares
Downloads
0
Comments
4
Likes
8

Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide
  • Mor… joint work with Set up live demo? http://techdev3.search.corp.yahoo.com/semanticzoom/sf2.html

Transcript

  • 1. Generating Summaries and Visualization for Large Collections of Geo-referenced Photographs Alexander Jaffe*, Mor Naaman *, Tamir Tassa † , Marc Davis $ *Yahoo! Research Berkeley † Open University of Israel $ Yahoo! Research
  • 2. Attraction Map of Paris
      • Stanley Milgram, 1976.
      • Psychological Maps of Paris
  • 3. Attraction Map of London
      • Jaffe et al, 2006.
  • 4. Information Overload?
      • Flickr “geotagged”
  • 5. Overview
    • Problem definition
    • Intuition for solution
    • Algorithm for summarization
    • Visualizing the dataset
    • Evaluation
    • Demo?
  • 6. Problem Definition
    • Dataset:
    • ( photo_id , user_id, latitude, longitude)
    • ( photo_id , tag )
    • Result:
    • (photo_id, rank)
      • Given all photos from a geographic region, find a “representative” summary set
  • 7. Issues to Tackle
    • Noisy data
    Whatever, color, city, spectrum, santa barbara, california, usa, Lookatme, Herbert Bayer Chromatic Gate
    • Photographer biases
      • In locations
      • In Tags
    • Wrong data
  • 8. Intuition
      • More “activity” in a certain location indicates importance of that location
      • Tag that are unique to a certain location can suggest importance of that location
  • 9. (Very) Simple Example
  • 10. Algorithm Overview
    • Hierarchical Clustering of the location data
    • For each cluster, generate cluster score
    • Recursively generate ordering of all photos in each cluster, based on subcluster score and ordering
  • 11. The Clustered Return of the (Very) Simple Example! 4, 6, 5 8,7 4,8,6,5,7 20 10
  • 12. Generating a Summary
    • A complete ranking is produced for all photos in the dataset
    • An n -photo summary is simply the first n photos in this ranking.
  • 13. Generating Cluster Scores
    • Main Factors:
      • Number of photos
      • Relevance (bias) factors
      • “ Tag Distinguishability”
      • “ Photographer Distinguishability”
  • 14. Tag Distinguishability
    • A measure of uniqueness of concepts represented in the cluster (“document”)
    • TF/IDF based
      • Compute frequency of each tag (TF)
      • Compute (inverse) frequency of tag in the rest of the dataset (IDF)
      • Aggregate TF/IDF over all tags in cluster using L2 norm
    • Or, if you like formulas:
    Read the damn paper!
  • 15. Summary of San Francisco Golden Gate Bridge TransAmerica AT&T Baseball Park Golden Gate Twin Peaks Golden Gate Bay Bridge Ocean Beach Chinatown
  • 16. Progress Bar (almost done)
    • Problem definition
    • Intuition for solution
    • Algorithm for summarization
    • Visualizing the dataset
    • Evaluation
    • Demo?
  • 17. Tag Maps
    • Observation:
      • The algorithm identifies “representative” locations
      • The algorithm identifies unique, important tags
    Can be used to visualize the dataset!
  • 18. Tag Maps
  • 19. Tag Maps
  • 20. Ok, how do we evaluate this?
    • Direct human-evaluation of algorithmic results
      • Evaluated Tag Maps with various weighting options
      • Compared summaries to 3 base conditions
    • Compared chosen locations to top 15 locations selected by humans (Milgram-style)
  • 21. Maybe we have time for a demo
  • 22. Maybe we have time for Q’s
    • http://zonetag.research.yahoo.com
    • (applied in prototype cameraphone app)
    • http://blog.yahooresearchberkeley.com
    • (more on this and other topics)
    • Become an intern, get involved:
    • Email me.
    • Mor Naaman
    • [email_address]