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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

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

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Page 1: Preliminary Exploration of the Use of Geographical Information for Content-based Geo-tagging of Social Video

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

Page 2: Preliminary Exploration of the Use of Geographical Information for Content-based Geo-tagging of Social Video

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

Page 3: Preliminary Exploration of the Use of Geographical Information for Content-based Geo-tagging of Social Video

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

Page 4: Preliminary Exploration of the Use of Geographical Information for Content-based Geo-tagging of Social Video

4Visual similarity measures for semantic video retrieval

Different Division Methods

• Baseline

Different Division Methods

Page 5: Preliminary Exploration of the Use of Geographical Information for Content-based Geo-tagging of Social Video

5Visual similarity measures for semantic video retrieval

• Temperature Data based

Different Division Methods

Page 6: Preliminary Exploration of the Use of Geographical Information for Content-based Geo-tagging of Social Video

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.

Page 7: Preliminary Exploration of the Use of Geographical Information for Content-based Geo-tagging of Social Video

7Visual similarity measures for semantic video retrieval

• Biomes Data based

Different Division Methods

Page 8: Preliminary Exploration of the Use of Geographical Information for Content-based Geo-tagging of Social Video

8Visual similarity measures for semantic video retrieval

Run Results

Run Results

Page 9: Preliminary Exploration of the Use of Geographical Information for Content-based Geo-tagging of Social Video

9Visual similarity measures for semantic video retrieval

Run Results

Run Results

22 Biomes classification: 12.17% (random, 4.55%)

Page 10: Preliminary Exploration of the Use of Geographical Information for Content-based Geo-tagging of Social Video

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%)

Page 11: Preliminary Exploration of the Use of Geographical Information for Content-based Geo-tagging of Social Video

11Visual similarity measures for semantic video retrieval

Discussion

Indoor (42%)

Page 12: Preliminary Exploration of the Use of Geographical Information for Content-based Geo-tagging of Social Video

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

13Visual similarity measures for semantic video retrieval

Discussion

Normal (26%)

Page 14: Preliminary Exploration of the Use of Geographical Information for Content-based Geo-tagging of Social Video

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

Page 15: Preliminary Exploration of the Use of Geographical Information for Content-based Geo-tagging of Social Video

15Visual similarity measures for semantic video retrieval

Thank you!

[email protected]