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Preliminary Exploration of the Use ofGeographical Information for Content-based Geo-tagging of Social Video5-10-2012Xincha...
System Overview• Goal   derive location information from the visual content of videos• Challenge   • no tags: 35.7%, only ...
•Assumption   divide the world map into regions that have a high within-   region visual stability and a high between-regi...
Different Division Methods • Baseline              Visual similarity measures for semantic video Methods                  ...
• Temperature Data based                Visual similarity measures for semantic video Methods                             ...
• Temperature Data based6 temperature regions: from -20◦C to 40◦C with 10◦C intervals.                     Visual similari...
• Biomes Data based                Visual similarity measures for semantic video Methods                                  ...
Run Results                                                        Run Results              Visual similarity measures for...
Run Results    22 Biomes classification: 12.17% (random, 4.55%)                                                          R...
Discussion• Visual Content of Test Videos   500 videos from the 4182 videos (12%)   • Indoor (42%)   • Outdoor Event (32%)...
Indoor (42%)                                           DiscussionVisual similarity measures for semantic video retrieval  ...
Outdoor Event (32%)                                           DiscussionVisual similarity measures for semantic video retr...
Normal (26%)                                           DiscussionVisual similarity measures for semantic video retrieval  ...
Conclusion and Future work • Recall our assumption    “we can divide the world map into regions    that have a high within...
Thank you!                                        X.Li-3@tudelft.nl  Visual similarity measures for semantic video retriev...
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Preliminary Exploration of the Use of Geographical Information for Content-based Geo-tagging of Social Video

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Preliminary Exploration of the Use of Geographical Information for Content-based Geo-tagging of Social Video

  1. 1. Preliminary Exploration of the Use ofGeographical Information for Content-based Geo-tagging of Social Video5-10-2012Xinchao Li, Claudia Hauff, Martha Larson, Alan Hanjalic Delft University of Technology Challenge the future
  2. 2. System Overview• Goal derive location information from the visual content of videos• Challenge • no tags: 35.7%, only one tag: 13.1% • improve metadata-based system System Overview Visual similarity measures for semantic video retrieval 2
  3. 3. •Assumption divide the world map into regions that have a high within- region visual stability and a high between-region variability South Pole Great Victoria Desert System Overview Visual similarity measures for semantic video retrieval 3
  4. 4. Different Division Methods • Baseline Visual similarity measures for semantic video Methods Different Division retrieval 4
  5. 5. • Temperature Data based Visual similarity measures for semantic video Methods Different Division retrieval 5
  6. 6. • Temperature Data based6 temperature regions: from -20◦C to 40◦C with 10◦C intervals. Visual similarity measures for semantic video Methods Different Division retrieval 6
  7. 7. • Biomes Data based Visual similarity measures for semantic video Methods Different Division retrieval 7
  8. 8. Run Results Run Results Visual similarity measures for semantic video retrieval 8
  9. 9. Run Results 22 Biomes classification: 12.17% (random, 4.55%) Run Results Visual similarity measures for semantic video retrieval 9
  10. 10. Discussion• Visual Content of Test Videos 500 videos from the 4182 videos (12%) • Indoor (42%) • Outdoor Event (32%) • Normal Outdoor (26%)• Visual Content of Training Photos 458 photos from the 3M training set • Indoor (27.5%) Discussion Visual similarity measures for semantic video retrieval 10
  11. 11. Indoor (42%) DiscussionVisual similarity measures for semantic video retrieval 11
  12. 12. Outdoor Event (32%) DiscussionVisual similarity measures for semantic video retrieval 12
  13. 13. Normal (26%) DiscussionVisual similarity measures for semantic video retrieval 13
  14. 14. Conclusion and Future work • Recall our assumption “we can divide the world map into regions that have a high within-region visual stability and a high between-region variability.” • indoor images are noisy information • Only use outdoor videos to train and test Discussion Visual similarity measures for semantic video retrieval 14
  15. 15. Thank you! X.Li-3@tudelft.nl Visual similarity measures for semantic video retrieval 15

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