• Save
Tag Maps
Upcoming SlideShare
Loading in...5
×

Like this? Share it with your network

Share

Tag Maps

  • 8,762 views
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
8,762
On Slideshare
8,657
From Embeds
105
Number of Embeds
5

Actions

Shares
Downloads
0
Comments
4
Likes
8

Embeds 105

http://yahooresearchberkeley.com 101
http://72.14.203.104 1
http://sandra-informacao.blogspot.com 1
http://209.85.135.104 1
http://www.slideshare.net 1

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]