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Loud and Trendy: Crowdsourcing
Impressions of Social Ambiance
in Popular Indoor Urban Places
Darshan Santani, Daniel Gatica-Perez
Idiap and EPFL Switzerland
28 October 2015
ACM MM 2015, Brisbane
Which place looks more “romantic”?
A B
Which place feels more “loud”?
A B
Understanding Social Ambiance of Urban Places
Place impressions define our favorite hangouts and
shape our new discoveries
Social ambiance elicited by perceptual cues and prior
knowledge
Connections between psychological features of cities
and key indicators like well-being and prosperity
Our goal: to provide “a better idea of how people
perceive and experience places”
Brave New Idea?
Social
Media
Mobile
Technologies
Lots of images
Lighting
Spatial Layout
Flooring
Wall Decorations
Table Layout
Ceiling
Holistic study of place ambiance would involve
interpretation of multiple sources of information,
including subtle visual (and audio) cues → Many
challenges for multimedia research
Open Issue: Multimedia approaches to study social
perception of urban places.
Research Questions
● RQ1: What types of social media images are perceived as being
more informative of the ambiance of popular indoor places?
● RQ2: Can the ambiance of an indoor place be reliably assessed
by observers of social media images? If so, for what dimensions
of ambiance?
Dataset – Places
Singapore
NYC
Seattle
Mexico City
Barcelona Paris
50 popular places per city
Places: cafes, restaurants, bars, or clubs
Dataset – Places
Singapore
NYC
Seattle
Mexico City
Barcelona
Paris
50 popular places per city
Places: cafes, restaurants, bars, or clubs
Dataset – Places
Singapore
NYC
Seattle
Mexico City
Barcelona
Paris
50 popular places per city
Places: cafes, restaurants, bars, or clubs
Image Dataset #1
● Random Image Corpus
● 50,000+ images for all 300 places via 4SQ API
● 55% iPhone; 19% Android; 22% via Instagram
● Randomly selected 3 images per place for a total of 900 images
Image Dataset #2
● Physical Environment Image Corpus
● Clear views of the environment (manually chosen)
● 3 images per place for a total of 900 images
RQ1: What types of social media images
best convey the ambiance of popular indoor
places?
Use image corpora to judge which image selection
approach results in images being perceived as more
adequate to convey ambiance.
Methodology
● Crowdsourcing via Mechanical Turk
● 5 images – 2 from the Physical Environment image
corpus, and 3 from Random image corpus
● Tasks:
● Rank the images based on how informative they are of the
ambiance of a place
● Categorize the images in one of the four classes:
– Food/Drinks,
– People/Group,
– Physical Environment, and
– None of these.
Results
Images from Phy.Env.
corpus are in Top 2 ranks
91.7% for ambiance
Results
Images from Phy.Env.
corpus are in Top 2 ranks
91.7% for ambiance
96% of Phy. Env. Image corpus
describe the physical environment
67% Random image corpus describe
either food or people
Results
Phy. Env. image corpus describe the
physical environment in 96.2% of the cases
Random image corpus describe food items
or people in 67% of the cases
Finding from RQ1: Images with clear
views of the environment are perceived
as more suitable to characterize indoor
ambiance.
RQ2: Can the ambiance of an indoor place
be reliably assessed by observers of social
media images? If so, for what dimensions of
ambiance?
Methodology
● Crowdsourcing via Mechanical Turk
● Data: Phy. Env. Corpus 900 images across 300 places
● Ambiance Labels: 13 dimensions [Graham, 2011]
● Task: Rate personal impressions of the place ambiance along
13 dimensions on a 5-point Likert scale
● 10 annotations for each dimension per place → 3,000
responses.
[Graham 2011] L. T. Graham, & S. D. Gosling, “Can the ambiance of a place be determined by the user profiles of the
people who visit it?”. In the Fifth International AAAI Conference on Web and Social Media (ICWSM), 2011
Artsy
Bohemian
Conservative
Creepy
Dingy
Formal
Sophisticated
Loud
Old Fashioned
Off the beaten path
Romantic
Trendy
Upscale
Ambiance
Labels
Inter-annotator Consensus
[0.8, 1.0)
[0.6, 0.8)
[0.0, 0.6)
ICCs across Cities
[0.8, 1.0) [0.6, 0.8) [0.0, 0.6)Color Legend
Ambiance across Cities – Correlation Analysis
Comparison between Cities
● Relatively few significant differences across cities
● Popular places in Seattle are perceived as less artsy compared to places in
Barcelona.
● Popular places in Paris are perceived as less old fashioned com-pared to NYC
and Seattle.
Comparison between Cities
● Popular places in Seattle are perceived as less artsy compared to
places in Barcelona.
● Popular places in Paris are perceived as less old fashioned com-
pared to NYC and Seattle.
Findings from RQ2:
Reliable estimates of ambiance can be obtained
using social media images, suggesting the
presence of visual cues to form place impressions.
Most aggregate impressions of ambiance are
similar across popular places in all cities
Future Work
● What specific cues are used to form place impressions?
● Color? Lighting? Spatial layout? Interior Design?
● Automatic recognition of ambiance
● Establishing links between visual cues and place
ambiance
Q & A
Email: dsantani@idiap.ch
Twitter: @SabMayaHai

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Crowdsourcing Social Ambiance from Photos

  • 1. Loud and Trendy: Crowdsourcing Impressions of Social Ambiance in Popular Indoor Urban Places Darshan Santani, Daniel Gatica-Perez Idiap and EPFL Switzerland 28 October 2015 ACM MM 2015, Brisbane
  • 2. Which place looks more “romantic”? A B
  • 3. Which place feels more “loud”? A B
  • 4. Understanding Social Ambiance of Urban Places Place impressions define our favorite hangouts and shape our new discoveries Social ambiance elicited by perceptual cues and prior knowledge Connections between psychological features of cities and key indicators like well-being and prosperity Our goal: to provide “a better idea of how people perceive and experience places”
  • 7.
  • 9. Holistic study of place ambiance would involve interpretation of multiple sources of information, including subtle visual (and audio) cues → Many challenges for multimedia research Open Issue: Multimedia approaches to study social perception of urban places.
  • 10. Research Questions ● RQ1: What types of social media images are perceived as being more informative of the ambiance of popular indoor places? ● RQ2: Can the ambiance of an indoor place be reliably assessed by observers of social media images? If so, for what dimensions of ambiance?
  • 11. Dataset – Places Singapore NYC Seattle Mexico City Barcelona Paris 50 popular places per city Places: cafes, restaurants, bars, or clubs
  • 12. Dataset – Places Singapore NYC Seattle Mexico City Barcelona Paris 50 popular places per city Places: cafes, restaurants, bars, or clubs
  • 13. Dataset – Places Singapore NYC Seattle Mexico City Barcelona Paris 50 popular places per city Places: cafes, restaurants, bars, or clubs
  • 14. Image Dataset #1 ● Random Image Corpus ● 50,000+ images for all 300 places via 4SQ API ● 55% iPhone; 19% Android; 22% via Instagram ● Randomly selected 3 images per place for a total of 900 images
  • 15. Image Dataset #2 ● Physical Environment Image Corpus ● Clear views of the environment (manually chosen) ● 3 images per place for a total of 900 images
  • 16. RQ1: What types of social media images best convey the ambiance of popular indoor places? Use image corpora to judge which image selection approach results in images being perceived as more adequate to convey ambiance.
  • 17. Methodology ● Crowdsourcing via Mechanical Turk ● 5 images – 2 from the Physical Environment image corpus, and 3 from Random image corpus ● Tasks: ● Rank the images based on how informative they are of the ambiance of a place ● Categorize the images in one of the four classes: – Food/Drinks, – People/Group, – Physical Environment, and – None of these.
  • 18. Results Images from Phy.Env. corpus are in Top 2 ranks 91.7% for ambiance
  • 19. Results Images from Phy.Env. corpus are in Top 2 ranks 91.7% for ambiance 96% of Phy. Env. Image corpus describe the physical environment 67% Random image corpus describe either food or people
  • 20. Results Phy. Env. image corpus describe the physical environment in 96.2% of the cases Random image corpus describe food items or people in 67% of the cases Finding from RQ1: Images with clear views of the environment are perceived as more suitable to characterize indoor ambiance.
  • 21. RQ2: Can the ambiance of an indoor place be reliably assessed by observers of social media images? If so, for what dimensions of ambiance?
  • 22. Methodology ● Crowdsourcing via Mechanical Turk ● Data: Phy. Env. Corpus 900 images across 300 places ● Ambiance Labels: 13 dimensions [Graham, 2011] ● Task: Rate personal impressions of the place ambiance along 13 dimensions on a 5-point Likert scale ● 10 annotations for each dimension per place → 3,000 responses. [Graham 2011] L. T. Graham, & S. D. Gosling, “Can the ambiance of a place be determined by the user profiles of the people who visit it?”. In the Fifth International AAAI Conference on Web and Social Media (ICWSM), 2011
  • 25. ICCs across Cities [0.8, 1.0) [0.6, 0.8) [0.0, 0.6)Color Legend
  • 26. Ambiance across Cities – Correlation Analysis
  • 27. Comparison between Cities ● Relatively few significant differences across cities ● Popular places in Seattle are perceived as less artsy compared to places in Barcelona. ● Popular places in Paris are perceived as less old fashioned com-pared to NYC and Seattle.
  • 28. Comparison between Cities ● Popular places in Seattle are perceived as less artsy compared to places in Barcelona. ● Popular places in Paris are perceived as less old fashioned com- pared to NYC and Seattle. Findings from RQ2: Reliable estimates of ambiance can be obtained using social media images, suggesting the presence of visual cues to form place impressions. Most aggregate impressions of ambiance are similar across popular places in all cities
  • 29. Future Work ● What specific cues are used to form place impressions? ● Color? Lighting? Spatial layout? Interior Design? ● Automatic recognition of ambiance ● Establishing links between visual cues and place ambiance
  • 30. Q & A Email: dsantani@idiap.ch Twitter: @SabMayaHai