18-05-2022 Challenge the future Delft University of Technology Preliminary Exploration of the Use of Geographical Information for Content-based Geo-tagging of Social Video Xinchao Li, Claudia Hauff, Martha Larson, Alan Hanjalic
Nov 07, 2014
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
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
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
4Visual similarity measures for semantic video retrieval
Different Division Methods
• Baseline
Different Division Methods
5Visual similarity measures for semantic video retrieval
• Temperature Data based
Different Division Methods
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.
7Visual similarity measures for semantic video retrieval
• Biomes Data based
Different Division Methods
8Visual similarity measures for semantic video retrieval
Run Results
Run Results
9Visual similarity measures for semantic video retrieval
Run Results
Run Results
22 Biomes classification: 12.17% (random, 4.55%)
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%)
11Visual similarity measures for semantic video retrieval
Discussion
Indoor (42%)
12Visual similarity measures for semantic video retrieval
Discussion
Outdoor Event (32%)
13Visual similarity measures for semantic video retrieval
Discussion
Normal (26%)
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