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Tag Maps

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

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

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  • I saw that. Cool.
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  • Update: Check out the live version!
    http://tagmaps.research.yahoo.com
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  • 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 :)
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  • 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]

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