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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 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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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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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


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Outline

  • Motivation & Challenge

  • Our Solution

    • Framework Overview

    • Data Source

    • Demo

  • Conclusions and Future Work


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Outline

  • Motivation & Challenge

  • Our Solution

    • Framework Overview

    • Data Source

    • Demo

  • Conclusions and Future Work


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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


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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


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Why Travelogue Visualization is difficult

  • Travelogue De-noising

    • Location-oriented

    • Context words

  • Image Retrieval and Ranking

    • Semantic Gap between texts and images


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Outline

  • Motivation & Challenge

  • Our Solution

    • Framework Overview

    • Data Source

    • Demo

  • Conclusions and Future Work


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  • log-based model

  • log- tag model

  • tag- based model


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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


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Similarity Measures

  • Tag-Model

    • Tags also are UGC

    • De-noise tags

Topic Space


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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)


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  • 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


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http://202.113.2.198

  • Images are ranked based on the following three points:

  • image quality

  • log-tag similarity

  • Image diversity


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Demo


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Demo


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Outline

  • Motivation & Challenge

  • Our Solution

    • Framework Overview

    • Data Source

    • Demo

  • Conclusions and Future Work


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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


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LBSN’ 2009

Nov.3, 2009, Seattle, WA, USA

Thank you!


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