Preliminary Exploration of the Use of Geographical Information for Content-based Geo-tagging of...
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![Page 1: Preliminary Exploration of the Use of Geographical Information for Content-based Geo-tagging of Social Video](https://reader034.fdocuments.us/reader034/viewer/2022051816/545ccc27b0af9fa92c8b4a95/html5/thumbnails/1.jpg)
08-04-2023
Challenge the future
DelftUniversity ofTechnology
Preliminary Exploration of the Use of Geographical Information for Content-based Geo-tagging of Social VideoXinchao Li, Claudia Hauff, Martha Larson, Alan Hanjalic
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2Visual similarity measures for semantic video retrieval
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
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3Visual similarity measures for semantic video retrieval
Great Victoria Desert
South Pole
System Overview
• Assumption
divide the world map into regions that have a high within-region visual stability and a high between-region variability
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4Visual similarity measures for semantic video retrieval
Different Division Methods
• Baseline
Different Division Methods
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5Visual similarity measures for semantic video retrieval
• Temperature Data based
Different Division Methods
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6Visual similarity measures for semantic video retrieval
• Temperature Data based
Different Division Methods
6 temperature regions: from -20◦C to 40◦C with 10◦C intervals.
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7Visual similarity measures for semantic video retrieval
• Biomes Data based
Different Division Methods
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8Visual similarity measures for semantic video retrieval
Run Results
Run Results
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9Visual similarity measures for semantic video retrieval
Run Results
Run Results
22 Biomes classification: 12.17% (random, 4.55%)
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10Visual similarity measures for semantic video retrieval
Discussion
• Visual Content of Test Videos
• Indoor (42%)
• Outdoor Event (32%)
• Normal Outdoor (26%)
• Visual Content of Training Photos
458 photos from the 3M training set
• Indoor (27.5%)
Discussion
500 videos from the 4182 videos (12%)
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11Visual similarity measures for semantic video retrieval
Discussion
Indoor (42%)
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12Visual similarity measures for semantic video retrieval
Discussion
Outdoor Event (32%)
![Page 13: Preliminary Exploration of the Use of Geographical Information for Content-based Geo-tagging of Social Video](https://reader034.fdocuments.us/reader034/viewer/2022051816/545ccc27b0af9fa92c8b4a95/html5/thumbnails/13.jpg)
13Visual similarity measures for semantic video retrieval
Discussion
Normal (26%)
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14Visual similarity measures for semantic video retrieval
• 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
Conclusion and Future work