This document summarizes research on using tags and metadata from Flickr photos to build models of locations and derive the semantics of tags. It describes experiments with a dataset of 42,000 geotagged photos of San Francisco to cluster photos by location and score tags based on metrics like TF-IDF to generate "tag maps" that depict important places and landmarks for a given city. The research aims to improve image search, develop automated gazetteers of places and events, and associate time/place data to tags for richer understanding of content in large photo collections.
Digital tools that facilitate conversations: Understanding the social health ...craig lefebvre
An approach to thinking about the social revolution in preventive health and healthcare. Offers a way to think about these changes, how they impact existing social relationships, and what can be done to move towards a social health experience for all participants.
Digital tools that facilitate conversations: Understanding the social health ...craig lefebvre
An approach to thinking about the social revolution in preventive health and healthcare. Offers a way to think about these changes, how they impact existing social relationships, and what can be done to move towards a social health experience for all participants.
Workshop support session at Beyond Enterprise 2.0 conference - Amsterdam January 2012 - Inccreasing collaboration and expertise sharing throught social and innovative initiatives
FenEx Express was established in 2015 as a business dedicated to provide a guaranteed same day courier service to commerce and industry.specializes in “Hand Carried Services” to Asia, Africa, Europe and Middle East countries, Canada, U.S.A. FenEx is sister concern of MFIL. Our international staffs have had in excess of 20 years of experience in international Hand Carried Services for DOOR to door deliveries.
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Malaysia keynote "Ubiquitous Computing and Online Collaboration for Open Educ...Steve McCarty
"Ubiquitous Computing and Online Collaboration for Open Education." Keynote Address at the 5th International Malaysian Educational Technology Convention, Kuantan, Malaysia (17 October 2011).
Workshop support session at Beyond Enterprise 2.0 conference - Amsterdam January 2012 - Inccreasing collaboration and expertise sharing throught social and innovative initiatives
FenEx Express was established in 2015 as a business dedicated to provide a guaranteed same day courier service to commerce and industry.specializes in “Hand Carried Services” to Asia, Africa, Europe and Middle East countries, Canada, U.S.A. FenEx is sister concern of MFIL. Our international staffs have had in excess of 20 years of experience in international Hand Carried Services for DOOR to door deliveries.
Over the last 2 years, FenEx Express has grown to now provide a comprehensive logistical solution to facilitate our clients' every requirement.
Our business has developed through our reputation for close, personalized working relationships with all our customers, ensuring we always satisfy their needs and expectations.
Malaysia keynote "Ubiquitous Computing and Online Collaboration for Open Educ...Steve McCarty
"Ubiquitous Computing and Online Collaboration for Open Education." Keynote Address at the 5th International Malaysian Educational Technology Convention, Kuantan, Malaysia (17 October 2011).
Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
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Acetabularia Information For Class 9 .docxvaibhavrinwa19
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Model Attribute Check Company Auto PropertyCeline George
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The French Revolution Class 9 Study Material pdf free download
Columbia Talk: Landmark Search and Community-Contributed Multimedia
1. The Landmark Revolut ion:
I mproving I mage Search and
Explorat ion
f or Locat ion- Driven Queries
M or N aam an
Y ahoo! R esearch B erkeley
Y ahoo! A dvanced D evelopm ent D i si
vi on
2. How Flickr Helps us Make Sense of t he
World:
Cont ext and Cont ent in Communit y-
Cont ribut ed
Media Collect ions
M or N aam an
Y ahoo! R esearch B erkeley
Y ahoo! A dvanced D evelopm ent D i si
vi on
3. Dat a Descript ion
Lyndon Kennedy, Mor Naaman
3 | Y!ADD, 2007
4. Tag Pat t erns
Lyndon Kennedy, Mor Naaman
4 | Y!ADD, 2007
5. Tag Pat t erns
Lyndon Kennedy, Mor Naaman
5 | Y!ADD, 2007
6. Tag Pat t erns
Lyndon Kennedy, Mor Naaman
6 | Y!ADD, 2007
7. Tag Pat t erns
Lyndon Kennedy, Mor Naaman
7 | Y!ADD, 2007
8. Tag Pat t erns
Lyndon Kennedy, Mor Naaman
8 | Y!ADD, 2007
9. Tag Pat t erns
Lyndon Kennedy, Mor Naaman
9 | Y!ADD, 2007
10. Communit y- cont ribut ed: Bet t er Dat a?
• M edi a
• D escri ve text (ti e , capti , tag)
pti tl on
• Di scussions and com m ents
• V i s and vi patterns
ew ew
• Item use and feedback
• R euse and rem ix
• M i - and expl ci recom m endati
cro it ons
• “ontext M etadata”
C
•…
Lyndon Kennedy, Mor Naaman
10 | Y!ADD, 2007
11. Pat t erns That Make Sense
• S em anti space
c
• A cti ty and vi i data
vi ew ng
• U ser/ personaldata
• S ocialnetw ork
• Locat ion/ t ime
Lyndon Kennedy, Mor Naaman
11 | Y!ADD, 2007
12. Tag Pat t erns: Beyond Geo
Lyndon Kennedy, Mor Naaman
12 | Y!ADD, 2007
14. Older Tigers?
• N o tigers, beaches
and sunsets.
ease .
Pl
Lyndon Kennedy, Mor Naaman
14 | Y!ADD, 2007
15. Research Challenges
• C ontent i sti lhard …
s l
• U nstructured data (no sem anti )
cs
• T ags, not ground truth labels
– F al negati and posi ves
se ve ti
– If that even m eans anything
• N oise
• S cale
– Com putation
– Long tai m pl es no supervi
li i sed learning
• B i / feedback / S pam
as
Lyndon Kennedy, Mor Naaman
15 | Y!ADD, 2007
16. That Noise….
• N oi data
sy
• Photographer biases
• W rong data
5 k ms
6 km s
Lyndon Kennedy, Mor Naaman
16 | Y!ADD, 2007
17. Foremost Challenge:
• W hat’s the user probl ?
em
– N avigati / expl
on oration
– R ecom m endation
– N ew appl cati
i on
– O ther?
• G rounded i realneeds
n
• W hat i pact on the
m
com m uni ?
ty
“Social Media Cycle”
Lyndon Kennedy, Mor Naaman
17 | Y!ADD, 2007
18. Talk Out line
• Visual ze
i
– Creati a W orl E xpl
ng d orer
• G enerate know ledge
– E xtracti T ag S em anti
ng cs
• S earch
– Landm ark search
Lyndon Kennedy, Mor Naaman
18 | Y!ADD, 2007
19. Surely, we can do bet t er t han t his
Flickr
“geot agged” in
San Francisco
Lyndon Kennedy, Mor Naaman
19 | Y!ADD, 2007
20. Simple Model
(phot o_ id, user_ id, t ime,
lat it ude, longit ude)
(phot o_ id, t ag)
Lyndon Kennedy, Mor Naaman
20 | Y!ADD, 2007
21. I nt uit ion
More “act ivit y” in a cert ain locat ion
indicat es import ance of t hat locat ion
Tag t hat are unique t o a cert ain locat ion
can represent t he locat ion bet t er
Lyndon Kennedy, Mor Naaman
21 | Y!ADD, 2007
22. Translat ion int o simple algorit hm
• Clusteri of photos
ng
• S cori of tags
ng
– T F / ID F / U F
Lyndon Kennedy, Mor Naaman
22 | Y!ADD, 2007
23. Tag Maps - SF
Lyndon Kennedy, Mor Naaman
23 | Y!ADD, 2007
24. At t ract ion Maps of Paris
S tanley
M i gram ,
l
1976.
”Psychological
Maps of Paris”
Lyndon Kennedy, Mor Naaman
24 | Y!ADD, 2007
25. At t ract ion Maps of Paris
Y !R B , 2006.
”Tag Maps:
World Explorer”
Lyndon Kennedy, Mor Naaman
25 | Y!ADD, 2007
26. Make a World Explorer
ht t p: / / t agmaps. research. yahoo. com
A l see [A hern et al J CD L 2007]
.,
so
Lyndon Kennedy, Mor Naaman
26 | Y!ADD, 2007
27. Summary of San Francisco
Golden Gat e Bridge TransAmerica
AT&T
Baseball Park
Golden Gat e
Twin Peaks
Golden Gat e
Ocean Beach Bay Bridge Chinat own
Lyndon Kennedy, Mor Naaman
27 | Y!ADD, 2007
28. Tag Maps - Paris - Les Blogs?
Lyndon Kennedy, Mor Naaman
28 | Y!ADD, 2007
29. Talk Out line
• Visual ze
i
– Creati a W orl E xpl
ng d orer
• G enerate know ledge
– E xtracti T ag S em anti
ng cs
• S earch
– Landm ark search
Lyndon Kennedy, Mor Naaman
29 | Y!ADD, 2007
30. Tag- based Modeling
• D eri m eani
ve ngfuldata about i vi
ndi dualtags
• B ased on the tag ’s m etadata patterns
• E .g., Yahoo! Mission College, SIGIR 2007.
Lyndon Kennedy, Mor Naaman
30 | Y!ADD, 2007
31. Ext ended Model
(phot o_ id, user_ id, t ime,
lat it ude, longit ude)
(phot o_ id, t ag)
(t ag, locat ion)
(t ag, t ime)
Lyndon Kennedy, Mor Naaman
31 | Y!ADD, 2007
32. Tag Pat t erns
Lyndon Kennedy, Mor Naaman
32 | Y!ADD, 2007
33. Tag Semant ics
• Im proved i age search through query sem anti
m cs
• A utom ati pl - and event-gazetteers
c ace
• A ssoci on of m i ng ti e / pl
ati ssi m ace data based on tags
•…
Lyndon Kennedy, Mor Naaman
33 | Y!ADD, 2007
34. San Francisco Experiment s
~43 k photos
~800 tags
San Francisco Dat aset :
42, 000 Phot os
800+ popular t ags
Lyndon Kennedy, Mor Naaman
34 | Y!ADD, 2007
35. Experiment s
Result s: BYOBW!
We can derive t ag semant ics using locat ion and t ime
met adat a.
[Rat t enbury et al, SI GI R 2007]
byobw
Lyndon Kennedy, Mor Naaman
35 | Y!ADD, 2007
36. Talk Out line
• Visual ze
i
– Creati a W orl E xpl
ng d orer
• G enerate know ledge
– E xtracti T ag S em anti
ng cs
• S earch
– Landm ark search
Lyndon Kennedy, Mor Naaman
36 | Y!ADD, 2007
37. Rolling in Cont ent
• S o far, w e leveraged m etadata patterns to find
– W hat are the geo-driven features
– W here peopl take photos of these features
e
• C an w e uti i
l zed content anal s?
ysi
• Hmmm….
Lyndon Kennedy, Mor Naaman
37 | Y!ADD, 2007
38. Handling scale
• R educe com putati requi
on rem ents
– F i ter usi m etadata
l ng
• U nsupervised m ethods
– E ffecti for l
ve ong tai i
lw thout trai ng
ni
Lyndon Kennedy, Mor Naaman
38 | Y!ADD, 2007
39. Building Visual Summaries
Raw Data Locations and Names
Visual Summary?
Lyndon Kennedy, Mor Naaman
39 | Y!ADD, 2007
40. The Problem, in Short
Find less of and more of t his…
t his…
… hout explicit ly
wit
knowing t he dif f erence.
Lyndon Kennedy, Mor Naaman
40 | Y!ADD, 2007
41. Locat ion can help
E nough visual
si i ari for
m l ty
earni ?
l ng
Lyndon Kennedy, Mor Naaman
41 | Y!ADD, 2007
43. Visual Feat ures
• Color: m om ents over a 5 x 5 grid
• Text ure: G abor over globali age
m
• I nt erest point s: S IF T
Lyndon Kennedy, Mor Naaman
43 | Y!ADD, 2007
44. Learning f rom noisy labels
Lyndon Kennedy, Mor Naaman
44 | Y!ADD, 2007
45. Clust ering
• K -m eans over l -l
ow evelfeatures
(texture and col )
or
• V ary val of K w i totalnum ber of photographs
ue th
(avg. cluster si ~ 20)
ze
Lyndon Kennedy, Mor Naaman
45 | Y!ADD, 2007
46. Ranking clust ers
• N um ber of users
– M ore users -> m ore shared interest
• T em poralspread
– Persistent over ti e -> m ore l kel to be locati , not event
m iy on
– Alternatel use m ethod descri
y bed earl er
i
• Visualcoherence
– M easure of diversi of vi
ty sualcluster
• Visualconnecti ty
vi
– M ore on thi l
s ater…
Lyndon Kennedy, Mor Naaman
46 | Y!ADD, 2007
48. Ranking images: low- level similarit y
E ucl dean di
i stance from
cluster centroi i col
dn or
and texture space .
Lyndon Kennedy, Mor Naaman
48 | Y!ADD, 2007
49. Ranking images: discriminat ive model
S am pl pseudo-
e
negati ves from outside
uster.
of cl
Learn S V M m odelover
col / texture space .
or
R ank by distance from
S V M m argi .
n
Lyndon Kennedy, Mor Naaman
49 | Y!ADD, 2007
50. Point - wise Linking
Lyndon Kennedy, Mor Naaman
50 | Y!ADD, 2007
51. Ranking images: point - wise links
F orm l nks betw een
i
i ages vi m atchi
m a ng
S IF T poi .
nts
R ank by degree of
connecti ty.
vi
Lyndon Kennedy, Mor Naaman
51 | Y!ADD, 2007
52. Landmark Graph St ruct ure
Less
connected
More
connected
Lyndon Kennedy, Mor Naaman
52 | Y!ADD, 2007
53. Coit Tower: Two Main Views
Shots from
Coit Tower
Far or
occluded
shots
Shots of
Coit Tower
Lyndon Kennedy, Mor Naaman
53 | Y!ADD, 2007
54. Ranking images: f usion
• S el -si i ari : E ucl dean di
f m l ty i stance from centroi i
dn
l -l
ow evelfeature space .
• Di m nati : di
scri i ve stance from S V M deci on
si
boundary.
• Poi -w i : degree of the photo
nt se
• Fusion: sum of scores, norm al zed vi si oi
i a gm d
function
Lyndon Kennedy, Mor Naaman
54 | Y!ADD, 2007
55. Result s: Palace of Fine Art s
X X
X
XX X
X
Tags-only Tags+Location Tags+Location+Visual
Lyndon Kennedy, Mor Naaman
55 | Y!ADD, 2007
56. Evaluat ion
• D ataset: geo-tagged B ay A rea photos from F l ckr
i
• S elect 10 landm arks to evaluate
• A ppl al thm (and basel ne ) to di
y gori i scover
representati i ages
ve m
Lyndon Kennedy, Mor Naaman
56 | Y!ADD, 2007
58. More Result s: Golden Gat e Bridge
X
X
X
X XX
XX X
T ags-onl T ags+Locati T ags+Locati +V i
y on on sual
Lyndon Kennedy, Mor Naaman
58 | Y!ADD, 2007
59. Evaluat ion I ssues
• Preci <> R epresentati
se ve
Lyndon Kennedy, Mor Naaman
59 | Y!ADD, 2007
60. Evaluat ion I ssues
• Preci <> D i
se verse
Lyndon Kennedy, Mor Naaman
60 | Y!ADD, 2007
63. Conclusions
• Locati i strong predi
on s ctor of content
• Landm arks and geo-rel ated queri can be i
es denti ed
fi
• C om puter vi on can w ork . S om eti es.
si m
Lyndon Kennedy, Mor Naaman
63 | Y!ADD, 2007
64. API s f or all!
• E verythi w e can do, you can do (better). A PIs
ng
i ude :
ncl
– Cel ow er ID database
lT
– S uggested T ags based on context
– T agM aps data
– T agM aps W idget
http://developer.yahoo.com/yrb/
Lyndon Kennedy, Mor Naaman
64 | Y!ADD, 2007
65. Thanks
With: L yndo K ennedy, S haneA hern, R ahul N air, T yeR attenbury, J eannieYang, N athan Good, S imon K ing.
n
In the papers: M IR 06, J CD L 07, S IG IR 07, M M 07
A l ask m e about: Z oneT ag , Z urfer, F i E agl
so re e
R ead more, follow: http://www.whyrb.com
P ast talks slides: http://slideshare.net/mor
M or N aaman
Lyndon Kennedy, Mor Naaman
65 | Y!ADD, 2007