What's in a place? Adventures with Location-Aware Media
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Transcript of What's in a place? Adventures with Location-Aware Media
1yahooresearchberkeley.comRahul Nair
What’s in a place?Adventures with Location-Aware Media
Rahul Nair
Yahoo! Research Berkeley
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Flickr “geotagged”
20+ million images
Can we do better?
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Talk Outline
• Extracting information from geo-tagged photos
• World Explorer: Visualization
• ZoneTag: Creation
• Zurfer: Consumption
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Attraction Map of Paris
Stanley Milgram, 1976. Psychological Maps of Paris
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Attraction Map of Paris
Y!RB, 2007.
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Location-driven Modeling
• Derive meaningful data about map regions
• E.g., representative tags, photos
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Data Description
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Issues
• Sparse data set
• Photographer bias
– In location
– In tags
• Incorrect data
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Heuristics
• Number of photographs denotes the “importance” of a location
• Users will use a common subset of tags to describe objects/locations
• Concentrated tag usage indicates descriptiveness
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Algorithm
• Clustering: k-Means, get set of k clusters
• “Document” C is bag of all tags in cluster
• For each tag in C calculate:
– TF = |P(C,t)|
– IDF = |P(R)| / |P(R, t)|
– UF = |U(C,t)|/|U(C)|
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Scoring
• Score (t) = TF * IDF * UF
• Threshold values
– 30+ photographs
– Minimum 3 users
– Score > 1
• Final dataset: (tag, score, latitude, longitude)
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Talk Outline
• Mining information from geo-tagged photos
• World Explorer: Visualization
• ZoneTag: Creation
• Zurfer: Consumption
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DEMO
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Precomputation
• Divide the world into equal sized non-overlapping tiles
• Compute and store the tags for each tile
• Repeat for different zoom
levels
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Retrieval
• Find the tile level closest in size to the request area
• Select the tiles that fully cover the request area
• Return the tags that fall within the request area
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User Study
10 subjects
• 6 female, 4 male
• Ages 20-60
• Varying technical knowledge
• No geotagged photos of their own
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Experiment tasks
• Vacation recap
• San Francisco tour
• Explore a new city
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Recall
Reminded the subject about locations
• “It brings out memories”
• “Oh my God! This place has the best restaurants”
• “We wanted to see the Polynesian Cultural Center"
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Discovery
Participants discovered previously unknown locations and events
– “I’ve never heard of this festival”
– “There is car racing which I'd probably go see”
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Needle & Haystack
• Excellent visualization of the Haystack
• Hard to find specific information
– “Where was Culver City again?”
• No way to search
– “I guess what I’m looking for are bull fighting pictures”
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Other Responses
• Gets the “vibe” of a place
• Share with other people
• Tags did not always match the mental model of a location
• Wanted more tags
• Want more info about tags
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Conclusions
• Extract meaningful aggregate information from georeferenced data
• Allows users to explore locations in a new way
• Users like using the overview but also want the ability to search
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Future work
• Adding search capability
• Show photos in places with no tags
• Differentiate locations and events
• Apply to other types of georeferenced data
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Talk Outline
• Mining information from geo-tagged photos
• World Explorer: Visualization
• ZoneTag: Creation
• Zurfer: Consumption
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Current Mobile Experience
• Difficult to share (or even save!)
• Hard to find
– No context
– No semantic information
Current mobile experience?
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Current Mobile Experience
• Difficult to share (or even save!)
• Hard to find
– No context
– No semantic information
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ZoneTag Experience
• 2-click upload (same key!)
• Photo uploaded with location and time metadata
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Where does location come from?
• Bluetooth GPS (when available)
• User-contributed cell tower mapping
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ZoneTag Experience
• Tagging made easy
– Tag/annotate your photos from the phone
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Where do tags come from?
• Tags I used in this context (`home’)
• Tags my friends used in this context
• Tags other people used in this context (‘Ricoh’, ‘California Research Center’)
– E.g., TagMaps data
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Where do tags come from?
• Stuff around you:
– Yahoo! Local
– Upcoming.org
• Stuff from you (any RSS 2.0 feed):
– Calendar
– Favorite hangouts (Wayfaring, Plazes, Socialight)
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Example of Tagging
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Talk Outline
• Mining information from geo-tagged photos
• World Explorer: Visualization
• ZoneTag: Creation
• Zurfer: Consumption
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Flickr “geotagged”
20+ million images
Can we do mobile?
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Strengths of Mobile
• Personal
• Easy to access
• Networked
• Context aware
The ultimate photo wallet
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Design Goals
• Engagement and Discovery
– Spatial
– Social
• Allow customization
• Complete access to your photos
• Search and Filtering capability
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Channel Metaphor
• Each row is a single channel
• Navigate using 4 way
– Left & right to browse channel (infinite scroll)
– Up & down to change channels
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Detail view
• Enlarge photo
– Scroll through channel
• Photo details
• Add comments
• Add to favourites
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Nearby photos channel
• Photos from the users current location
• Local Highlights
• My Nearby
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Social Channels
• Contacts photos
– Expanded view
• Recent Activity
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My Stuff Channel
• My Photos
– By Location
– By Tag
• My Photo Wallet
• My Favourites
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Custom Channels
• Users can create channels to match their interests
– Tags
– Groups
– Location aware (dogs near me)
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Search and Filter
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Try it out
http://zurfer.research.yahoo.com
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Conclusions
• It is possible to extract information from georeferenced media
• Users like browsing the extracted data
• It can help users tag new media
• We hope it helps them browse on a mobile device
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Questions?
Rahul [email protected]
http://tagmaps.research.yahoo.comhttp://zonetag.research.yahoo.com http://zurfer.research.yahoo.com