The Social Media Opportunityand Application for Landmark Search
Mor Naaman
Yahoo! Advanced Development Research
Mor Naaman
Social Media Patterns:
Semantic space (from any text)
Activity and viewing data
User/personal data
Social network
Location/time metadata
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E.g., Semantic Patterns
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Heard of Flickr?
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This is not An Arch
“Noisy” data
Photographer biases
Wrong data
6 kms5 kms
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Tag Patterns
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What is “Social Media”?
Online media published or shared by individuals and organizations, in an environment that encourages significant individual participation and that promotes curation, discussion and re-use.
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Social Media = Context
Context is kingPredictor of content
Modifies perception of content
Social media: context also predicts activity?
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Social Media = Challenge
Content is still hard…
Unstructured data (no semantics)
Tags, not ground truth labels
Noise
Scale • Computation
• Long tail means no supervised learning
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Social Media = Opportunity
New types of data / context
Openness/need for new applications and experiences
Social environment encourages engagement
Opportunity for MM!10
User
CommunityAnalyze, extract
patterns
Support, motivate
Encourage, enable
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Approach
Analyze context to extract patternsReduce content analysis to constrained scenario/task
Leverage content to improve metadata, relevance
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Location-driven Modeling
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Extracting Knowledge
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More “activity” in a certain locationindicates the importance of that location
Tags that are unique to a certain location can be used to represent the location
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Tag Maps - SF
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Make a World Explorer
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published in JCDL 2007
http://tagmaps.research.yahoo.com
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Better Image Search
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Approach
Analyze context to extract patternsReduce content analysis to constrained scenario/task
Leverage content to improve metadata, relevance
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Rolling in Content
We identified the landmarks...
We know where they are...
We can get the matching photos...
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System Overview
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published in WWW 2008
published in ACM MM 2007
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Learning from noisy labels
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Visual Features
•Color: moments over a 5x5 grid
•Texture: Gabor over global image
• Interest points: SIFT
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Ranking Clusters (1)
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Same “objects” that appear often in cluster’s photos suggest relevance
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Ranking Clusters (2)
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Similarity between photos inside cluster versus outside the cluster suggests coherence
Use Visual Features to compare average intra-cluster and inter-cluster similarity
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System Overview
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Sample Results: Golden Gate
Tags-only Tags+Location Tags+Location+Visual
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X
X
XX
XX
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Performance: PrecisionP
re
cis
ion
@ 1
0
0
0.25
0.50
0.75
1.00
alcatraz
baybridge
coittower
deyoung
ferrybuilding
goldengatebridge
lombardstreet
palaceoffinearts
sfmoma
transamerica
average
Tag-Only Tag-Location Tag-Visual Tag-Location-Visual
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+45% w/visual
+30% w/location
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Performance: RepresentativeR
ep
res
en
tati
ve
Ph
oto
s
0
2.5
5.0
7.5
10.0
alcatraz
baybridge
coittower
deyoung
ferrybuilding
goldengatebridge
lombardstreet
palaceoffinearts
sfmoma
transamerica
average
Tag-Only Tag-Location Tag-Visual
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Improve Relevance
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Repeated in Other Context
Analyze context to extract patternsReduce content analysis to constrained scenario/task
Leverage content to improve metadata, relevance
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Social Media @ Music Events
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Analyze context to get set of media items from a single event
Use content (AF) to robustly synchronize the clips
Increase relevance,
findability
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Social Media = Opportunity
To better understand media contentAnd robustly apply content analysis
To predict and enhance use and engagement
To invent the future of multimedia systems
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Thanks
With: Lyndon Kennedy, Tye Rattenbury, Alex Jaffe, Shane Ahern, Rahul Nair, Jeannie Yang
Some Slides: http://slideshare.net/mor
All photos/icons CC or with permission
http://infolab.stanford.edu/~mor
[email protected] , [email protected]
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Ranking images: low-level similarity
Euclidean distance from cluster centroid in color and texture space.
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Notes: Fire Eagle
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http://fireeagle.yahoo.net
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Landmark Graph Structure
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Close-up Shots of TowerLess-Connected PhotosHighly-Connected Photos Taken From Tower Far-away Shots
(a) Golden Gate Bridge (b) Coit Tower
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Results: Palace of Fine Arts
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X
XXXX
Tags-only Tags+Location Tags+Location+Visual
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