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New Opportunities for Understanding
Tourism Photography through AI
Dr Lyndon Nixon
School of Applied Data Science
MODUL University Vienna
lyndon.nixon@modul.ac.at
2
© 2011 MODUL University Vienna
Why care about visual destination brand?
3
© 2011 MODUL University Vienna
e.g. visual destination brand on Instagram
• Largest visual social network with 1.2
billion active users (1)
○ 62.8% of Instagrammers are aged
18-34
• Every week more than 1 million travel
related hashtags are searched (2)
• 48% of users use the visual content for
travel decision making and 35% use it to
discover new places (3)
(1) https://datareportal.com/reports/digital-2021-global-overview-report
(2) https://www.wexinc.com/insights/resources/u-s-travel-trends-2019-report/
(3) https://www.mediapost.com/publications/article/263167/instagram-the-place-to-be-for-
travel-brands.html
4
© 2011 MODUL University Vienna
The emergence of Computer Vision
• Researchers had to manually
annotate small samples of
visual media
• Neural Networks + Deep
Learning = significant
advances in computer vision
• Current benchmark: AI models
trained and tested on a large
generic image dataset
(ImageNet)
https://paperswithcode.com/sota/image-classification-on-imagenet
5
© 2011 MODUL University Vienna
Fine tuning a CV model for VDB
1. Determine a set of labels for cognitive
attributes of destination image which
are exclusive and exhaustive
2. Fine-tune a state-of-the-art CV
model for those labels by training it on
relevant destination photography
3. Demonstrate accuracy by evaluating
on a ground truth data set
6
© 2011 MODUL University Vienna
Fine tuning a CV model for VDB
7
© 2011 MODUL University Vienna
Fine tuning a CV model for VDB
8
© 2011 MODUL University Vienna
Measuring VDB with the classifier
▪ Our approach is to classify each photo
with a single label (class with the
highest confidence above a certain
threshold)
▪ The classification of an image data set
can be represented as a multi-
dimensional vector
▪ Bali’s vector is e.g. [0.07124011
0.04309587 0.10202287 0.01319261
0.02022867 0.03605981 0.06068602
0.06508355 0.09146878 0.01055409
0.01319261 0.01055409 0.09234828
0.05013193 0.08531223 0.0351803
0.02726473 0.17238347]
Try it at
https://huggingface.co/
lyndonnixon/destination
-image-classifier
9
© 2011 MODUL University Vienna
Study for DMO marketing
Bali #balitravel, #visitbali 1137
New
Orleans
#onetimeinnola 725
Marrakesh #visitmarrakech 934
Maldives #visitmaldives 1045
Paris #jetaimeparis 625
Dubai #visitdubai 813
Bora Bora #visit_borabora 1258
New York #itstimefornyc 745
Dubrovnik #lovedubrovnik 695
10
© 2011 MODUL University Vienna
Study for DMO marketing
11
© 2011 MODUL University Vienna
How this leads to smarter tourism
▪ DMOs and tourism stakeholders today lack vital information
on how their destinations (or tourism offers) are being
presented online through user generated image and video
▪ Visual classifiers offer a means to measure visual destination
brand accurately and at scale
BUT
▪ What is the right set of visual categories to use in quantifying
visual destination brand?
▪ How to deal with cultural and regional differences, both
across destinations and in terms of the viewer?
▪ We still miss studies on how (actual or intended) destination
brands relate (if at all ) to tourism metrics like number of
visitors, overnight stays, bookings, Website visits, etc.
12
© 2011 MODUL University Vienna
Tourism can only get smarter…
Image understanding
tech is more
accessible than ever.
ChatGPT 4 and other
tools will describe
images to you. Is this
the next step after
the visual classifier?
Screenshot from demo of BLIP: Bootstrapping
Language-Image Pre-training for Unified Vision-
Language Understanding and Generation at
https://huggingface.co/Salesforce/blip-image-
captioning-base
13
© 2011 MODUL University Vienna
THANK YOU for your attention
Dr Lyndon Nixon
lyndon.nixon@modul.ac.at

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New Opportunities for Understanding Tourist Photography.pptx

  • 1. New Opportunities for Understanding Tourism Photography through AI Dr Lyndon Nixon School of Applied Data Science MODUL University Vienna lyndon.nixon@modul.ac.at
  • 2. 2 © 2011 MODUL University Vienna Why care about visual destination brand?
  • 3. 3 © 2011 MODUL University Vienna e.g. visual destination brand on Instagram • Largest visual social network with 1.2 billion active users (1) ○ 62.8% of Instagrammers are aged 18-34 • Every week more than 1 million travel related hashtags are searched (2) • 48% of users use the visual content for travel decision making and 35% use it to discover new places (3) (1) https://datareportal.com/reports/digital-2021-global-overview-report (2) https://www.wexinc.com/insights/resources/u-s-travel-trends-2019-report/ (3) https://www.mediapost.com/publications/article/263167/instagram-the-place-to-be-for- travel-brands.html
  • 4. 4 © 2011 MODUL University Vienna The emergence of Computer Vision • Researchers had to manually annotate small samples of visual media • Neural Networks + Deep Learning = significant advances in computer vision • Current benchmark: AI models trained and tested on a large generic image dataset (ImageNet) https://paperswithcode.com/sota/image-classification-on-imagenet
  • 5. 5 © 2011 MODUL University Vienna Fine tuning a CV model for VDB 1. Determine a set of labels for cognitive attributes of destination image which are exclusive and exhaustive 2. Fine-tune a state-of-the-art CV model for those labels by training it on relevant destination photography 3. Demonstrate accuracy by evaluating on a ground truth data set
  • 6. 6 © 2011 MODUL University Vienna Fine tuning a CV model for VDB
  • 7. 7 © 2011 MODUL University Vienna Fine tuning a CV model for VDB
  • 8. 8 © 2011 MODUL University Vienna Measuring VDB with the classifier ▪ Our approach is to classify each photo with a single label (class with the highest confidence above a certain threshold) ▪ The classification of an image data set can be represented as a multi- dimensional vector ▪ Bali’s vector is e.g. [0.07124011 0.04309587 0.10202287 0.01319261 0.02022867 0.03605981 0.06068602 0.06508355 0.09146878 0.01055409 0.01319261 0.01055409 0.09234828 0.05013193 0.08531223 0.0351803 0.02726473 0.17238347] Try it at https://huggingface.co/ lyndonnixon/destination -image-classifier
  • 9. 9 © 2011 MODUL University Vienna Study for DMO marketing Bali #balitravel, #visitbali 1137 New Orleans #onetimeinnola 725 Marrakesh #visitmarrakech 934 Maldives #visitmaldives 1045 Paris #jetaimeparis 625 Dubai #visitdubai 813 Bora Bora #visit_borabora 1258 New York #itstimefornyc 745 Dubrovnik #lovedubrovnik 695
  • 10. 10 © 2011 MODUL University Vienna Study for DMO marketing
  • 11. 11 © 2011 MODUL University Vienna How this leads to smarter tourism ▪ DMOs and tourism stakeholders today lack vital information on how their destinations (or tourism offers) are being presented online through user generated image and video ▪ Visual classifiers offer a means to measure visual destination brand accurately and at scale BUT ▪ What is the right set of visual categories to use in quantifying visual destination brand? ▪ How to deal with cultural and regional differences, both across destinations and in terms of the viewer? ▪ We still miss studies on how (actual or intended) destination brands relate (if at all ) to tourism metrics like number of visitors, overnight stays, bookings, Website visits, etc.
  • 12. 12 © 2011 MODUL University Vienna Tourism can only get smarter… Image understanding tech is more accessible than ever. ChatGPT 4 and other tools will describe images to you. Is this the next step after the visual classifier? Screenshot from demo of BLIP: Bootstrapping Language-Image Pre-training for Unified Vision- Language Understanding and Generation at https://huggingface.co/Salesforce/blip-image- captioning-base
  • 13. 13 © 2011 MODUL University Vienna THANK YOU for your attention Dr Lyndon Nixon lyndon.nixon@modul.ac.at