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Xin Lu 1 , Yanwei Pang 1 *, Qiang Hao 1 , Lei Zhang 2 1 Tianjin University * Corresponding Author 2 Microsoft Research Asia November 3, 2009

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Xin Lu 1, Yanwei Pang 1, Qiang Hao 1, Lei Zhang 2 1 Tianjin University Corresponding Author 2 Microsoft Research Asia - PowerPoint PPT Presentation


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Visualize Textual Travelogue with Location-Relevant Images. Xin Lu 1 , Yanwei Pang 1 *, Qiang Hao 1 , Lei Zhang 2 1 Tianjin University * Corresponding Author 2 Microsoft Research Asia November 3, 2009. Outline. Motivation & Challenge Our Solution Framework Overview Data Source

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slide1

Visualize Textual Travelogue with Location-Relevant Images

Xin Lu 1, Yanwei Pang 1*, QiangHao1, Lei Zhang 2

1 Tianjin University

* Corresponding Author

2 Microsoft Research Asia

November 3, 2009

outline
Outline
  • Motivation & Challenge
  • Our Solution
    • Framework Overview
    • Data Source
    • Demo
  • Conclusions and Future Work
outline3
Outline
  • Motivation & Challenge
  • Our Solution
    • Framework Overview
    • Data Source
    • Demo
  • Conclusions and Future Work
what is a travelogue
What is a travelogue
  • What is a travelogue
    • Text/article that records one\'s travel experience
  • Where can we/you find travelogues
    • Blog, forum, Web2.0 community, etc.
  • What\'s the travelogue\'s difference from other text
    • User-generated content (UGC), rather than expert\'s articles large amount and booming informative to other tourists
why we visualize the travelogue
Why We Visualize the Travelogue
  • Travelogues are huge knowledge resources
    • People share others’ experience by reading travelogues online
  • Textual Travelogues
    • Long and noisy
    • Probably be written in foreign languages
  • Travelogue Visualizing
    • Highlight the useful information
    • Visualize the useful information
why travelogue visualization is difficult
Why Travelogue Visualization is difficult
  • Travelogue De-noising
    • Location-oriented
    • Context words
  • Image Retrieval and Ranking
    • Semantic Gap between texts and images
outline7
Outline
  • Motivation & Challenge
  • Our Solution
    • Framework Overview
    • Data Source
    • Demo
  • Conclusions and Future Work
slide8

log-based model

  • log- tag model
  • tag- based model
similarity measures
Similarity Measures
  • Log-Model & Log-tag Model
    • Log refers to travelogue
    • Context words

Great Wall: ancient times; stable; impregnable pass; No.1 in the world

Sanya Bay: sea sight; beach; sea food

similarity measures10
Similarity Measures
  • Tag-Model
    • Tags also are UGC
    • De-noise tags

Topic Space

data source
Data Source
  • 100K travelogues (automatically)
    • All written in Chinese
    • Downloaded from Ctrip
    • GPS data and English Name for the most popular 10K locations
  • 2500K images (automatically)
    • Flickr 950K (plenty of tags)
    • Picasa 300K (little tags)
    • *Google 1200K (includes snippets of the image)
slide12

We add 1200K images (includes snippets of the image) from Google

  • Flickr images are retrieved based on location
  • Google images are retrieved by “context words+ location”, which makes candidate images sets more relevant to the travelogue
slide13

http://202.113.2.198

  • Images are ranked based on the following three points:
  • image quality
  • log-tag similarity
  • Image diversity
outline16
Outline
  • Motivation & Challenge
  • Our Solution
    • Framework Overview
    • Data Source
    • Demo
  • Conclusions and Future Work
conclusions and future work
Conclusions and Future Work
  • Travelogue visualization benefit common people
    • Travelogue is more easily to understand
    • People all over the world benefit from others’ experience to plan trips
  • Future work
    • Further narrow the semantic gap using visual features
    • Improve evaluation approaches
thank you

LBSN’ 2009

Nov.3, 2009, Seattle, WA, USA

Thank you!

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