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Ghent and Cardiff University at the 2012 Placing Task

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Ghent and Cardiff University at the 2012 Placing Task

  1. 1. Ghent and Cardiff University at the 2012Placing Task (UG-CU)Olivier Van Laere, Bart DhoedtDepartment of Information Technology (INTEC)Ghent University, BelgiumSteven Schockaert, Jonathan A. Quinn, Frank C. LangbeinSchool of Computer Science & InformaticsCardiff University, United KingdomDepartment of Information Technology – Broadband Communication Networks (IBCN)MediaEval2012 Workshop, October 4-5, 2012, Pisa, Italy
  2. 2. Lessons from last year Using a prior in our language models that includes information from the user’s home location significantly boosts the results Clear need for a feature selection technique tailored to this task  E.g. WISTUD approach 2011Department of Information Technology – Broadband Communication Networks (IBCN) 2MediaEval2012 Workshop, October 4-5, 2012, Pisa, Italy
  3. 3. Lessons from last yearitaly Department of Information Technology – Broadband Communication Networks (IBCN) 3 MediaEval2012 Workshop, October 4-5, 2012, Pisa, Italy
  4. 4. Lessons from last yearsicily Department of Information Technology – Broadband Communication Networks (IBCN) 4 MediaEval2012 Workshop, October 4-5, 2012, Pisa, Italy
  5. 5. Lessons from last yearsea Department of Information Technology – Broadband Communication Networks (IBCN) 5 MediaEval2012 Workshop, October 4-5, 2012, Pisa, Italy
  6. 6. Lessons from last yearpisa Department of Information Technology – Broadband Communication Networks (IBCN) 6 MediaEval2012 Workshop, October 4-5, 2012, Pisa, Italy
  7. 7. Lessons from last yearleaningtower Department of Information Technology – Broadband Communication Networks (IBCN) 7 MediaEval2012 Workshop, October 4-5, 2012, Pisa, Italy
  8. 8. Lessons from last year Using a prior in our language models that includes information from the user’s home location significantly boosts the results Clear need for a feature selection technique tailored to this task  E.g. WISTUD approach 2011 Need for handling videos without tags  43.4% of test data compared to 16.1% last year We try to georeference the item at the 10000 clustering, and fall back to 2500, 500 in case of absence of textual informationDepartment of Information Technology – Broadband Communication Networks (IBCN) 8MediaEval2012 Workshop, October 4-5, 2012, Pisa, Italy
  9. 9. Data ~2.1M of the original ~3M task training photos Run 2: extracted SIFT features  to the extent the images were available on Flickr Run 5: ~17.1M Flickr photos  Crawled in 2011, accuracy 16 ~ street level Gazetteer: Google Geocoding API  Used to reverse geocode the “home” field from the user’s profile Department of Information Technology – Broadband Communication Networks (IBCN) 9 MediaEval2012 Workshop, October 4-5, 2012, Pisa, Italy
  10. 10. Approach – two step Data clustered into 500, 2500 and 10000 areas Feature vocabulary selection for each of those Language models are used to select most likely area to contain the given test video, based on textual information Similarity search, using the textual information, is used to select a location within this area based on the most similar training itemsDepartment of Information Technology – Broadband Communication Networks (IBCN) 10MediaEval2012 Workshop, October 4-5, 2012, Pisa, Italy
  11. 11. Approach – main differences Adopted feature selection method from WISTUD In case a video has no tags, use:  Textual home location from the user, video title and description as Flickr tags In case there is no textual info at all, default to London If available and considered reliable, include visual similarityDepartment of Information Technology – Broadband Communication Networks (IBCN) 11MediaEval2012 Workshop, October 4-5, 2012, Pisa, Italy
  12. 12. Approach – similarity search Instead of returning the location of most similar (using Jaccard index) training item 3 possible locations:  Most similar training photo  Home location of the owner (if allowed and available)  Visually most similar training photo We choose the location minimizing a certain scoreDepartment of Information Technology – Broadband Communication Networks (IBCN) 12MediaEval2012 Workshop, October 4-5, 2012, Pisa, Italy
  13. 13. Results and discussion - dev2011 1km 10km 100km 1000km 10K kmtestrun1 23.28% 44.62% 62.46% 75.00% 97.38%run2 24.20% 51.49% 72.62% 85.62% 97.85%run3 23.62% 49.84% 70.30% 84.14% 97.83%run4 0.04% 0.11% 0.92% 11.67% 81.02%run5 48.01% 65.98% 76.85% 87.38% 98.43%2012 1km 10km 100km 1000km 10K kmdevrun1 24.18% 53.13% 72.71% 85.15% 98.19%run2 24.65% 54.25% 75.05% 86.82% 98.34%run3 24.59% 54.25% 75.01% 86.82% 98.34%run4 0.58% 2.69% 5.82% 21.45% 92.07%run5 47.52% 66.04% 76.83% 86.65% Department of Information Technology – Broadband Communication Networks (IBCN) 97.66% 13 MediaEval2012 Workshop, October 4-5, 2012, Pisa, Italy
  14. 14. Results and discussion - dev2011 1km 10km 100km 1000km 10K kmtestrun1 23.28% 44.62% 62.46% 75.00% 97.38%run2 24.20% 51.49% 72.62% 85.62% 97.85%run3 23.62% 49.84% 70.30% 84.14% 97.83%run4 0.04% 0.11% 0.92% 11.67% 81.02%run5 48.01% 65.98% 76.85% 87.38% 98.43%2012 1km 10km 100km 1000km 10K kmdevrun1 24.18% 53.13% 72.71% 85.15% 98.19%run2 24.65% 54.25% 75.05% 86.82% 98.34%run3 24.59% 54.25% 75.01% 86.82% 98.34%run4 0.58% 2.69% 5.82% 21.45% 92.07%run5 47.52% 66.04% 76.83% 86.65% Department of Information Technology – Broadband Communication Networks (IBCN) 97.66% 14 MediaEval2012 Workshop, October 4-5, 2012, Pisa, Italy
  15. 15. Results and discussion - dev2011 1km 10km 100km 1000km 10K kmtestrun1 23.28% 44.62% 62.46% 75.00% 97.38%run2 24.20% 51.49% 72.62% 85.62% 97.85%run3 23.62% 49.84% 70.30% 84.14% 97.83%run4 0.04% 0.11% 0.92% 11.67% 81.02%run5 48.01% 65.98% 76.85% 87.38% 98.43%2012 1km 10km 100km 1000km 10K kmdevrun1 24.18% 53.13% 72.71% 85.15% 98.19%run2 24.65% 54.25% 75.05% 86.82% 98.34%run3 24.59% 54.25% 75.01% 86.82% 98.34%run4 0.58% 2.69% 5.82% 21.45% 92.07%run5 47.52% 66.04% 76.83% 86.65% Department of Information Technology – Broadband Communication Networks (IBCN) 97.66% 15 MediaEval2012 Workshop, October 4-5, 2012, Pisa, Italy
  16. 16. Results and discussion - dev2011 1km 10km 100km 1000km 10K kmtestrun1 23.28% 44.62% 62.46% 75.00% 97.38%run2 24.20% 51.49% 72.62% 85.62% 97.85%run3 23.62% 49.84% 70.30% 84.14% 97.83%run4 0.04% 0.11% 0.92% 11.67% 81.02%run5 48.01% 65.98% 76.85% 87.38% 98.43%2012 1km 10km 100km 1000km 10K kmdevrun1 24.18% 53.13% 72.71% 85.15% 98.19%run2 24.65% 54.25% 75.05% 86.82% 98.34%run3 24.59% 54.25% 75.01% 86.82% 98.34%run4 0.58% 2.69% 5.82% 21.45% 92.07%run5 47.52% 66.04% 76.83% 86.65% Department of Information Technology – Broadband Communication Networks (IBCN) 97.66% 16 MediaEval2012 Workshop, October 4-5, 2012, Pisa, Italy
  17. 17. Results and discussion - dev2011 1km 10km 100km 1000km 10K kmtestrun1 23.28% 44.62% 62.46% 75.00% 97.38%run2 24.20% 51.49% 72.62% 85.62% 97.85%run3 23.62% 49.84% 70.30% 84.14% 97.83%run4 0.04% 0.11% 0.92% 11.67% 81.02%run5 48.01% 65.98% 76.85% 87.38% 98.43%2012 1km 10km 100km 1000km 10K kmdevrun1 24.18% 53.13% 72.71% 85.15% 98.19%run2 24.65% 54.25% 75.05% 86.82% 98.34%run3 24.59% 54.25% 75.01% 86.82% 98.34%run4 0.58% 2.69% 5.82% 21.45% 92.07%run5 47.52% 66.04% 76.83% 86.65% Department of Information Technology – Broadband Communication Networks (IBCN) 97.66% 17 MediaEval2012 Workshop, October 4-5, 2012, Pisa, Italy
  18. 18. Results and discussion - test2012 test 1km 10km 100km 1000km 10K kmrun1 10.98% 28.10% 41.54% 57.91% 89.41%run2 11.36% 29.65% 47.18% 61.19% 89.98%run3 11.36% 29.65% 47.18% 61.19% 89.98%run4 0.10% 0.74% 2.56% 21.21% 91.37%run5 20.61% 34.24% 47.42% 59.47% 89.74% Department of Information Technology – Broadband Communication Networks (IBCN) 18 MediaEval2012 Workshop, October 4-5, 2012, Pisa, Italy
  19. 19. Results and discussion - test2012 test 1km 10km 100km 1000km 10K kmrun1 10.98% 28.10% 41.54% 57.91% 89.41%run2 11.36% 29.65% 47.18% 61.19% 89.98%run3 11.36% 29.65% 47.18% 61.19% 89.98%run4 0.10% 0.74% 2.56% 21.21% 91.37%run5 20.61% 34.24% 47.42% 59.47% 89.74% Department of Information Technology – Broadband Communication Networks (IBCN) 19 MediaEval2012 Workshop, October 4-5, 2012, Pisa, Italy
  20. 20. Results and discussion - test2012 test 1km 10km 100km 1000km 10K kmrun1 10.98% 28.10% 41.54% 57.91% 89.41%run2 11.36% 29.65% 47.18% 61.19% 89.98%run3 11.36% 29.65% 47.18% 61.19% 89.98%run4 0.10% 0.74% 2.56% 21.21% 91.37%run5 20.61% 34.24% 47.42% 59.47% 89.74% Department of Information Technology – Broadband Communication Networks (IBCN) 20 MediaEval2012 Workshop, October 4-5, 2012, Pisa, Italy
  21. 21. Results and discussion - test2012 test 1km 10km 100km 1000km 10K kmrun1 10.98% 28.10% 41.54% 57.91% 89.41%run2 11.36% 29.65% 47.18% 61.19% 89.98%run3 11.36% 29.65% 47.18% 61.19% 89.98%run4 0.10% 0.74% 2.56% 21.21% 91.37%run5 20.61% 34.24% 47.42% 59.47% 89.74% Department of Information Technology – Broadband Communication Networks (IBCN) 21 MediaEval2012 Workshop, October 4-5, 2012, Pisa, Italy
  22. 22. Conclusions Using textual home locations, title and description of the video, we can considerably improve the results SIFT features may help in some particular cases, but the computation cost seems hard to justify for this There seems to be scope for improving the results of feature selection techniques for tailored to this task  Witnessed by replacing chi-2 based method with the approach from WISTUD2011 Department of Information Technology – Broadband Communication Networks (IBCN) 22 MediaEval2012 Workshop, October 4-5, 2012, Pisa, Italy
  23. 23. Questions ?Olivier Van LaereOlivier.VanLaere@intec.ugent.bewww.ibcn.intec.ugent.beINTEC Broadband Communication Networks (IBCN)Department of Information Technology (INTEC)Ghent University - IBBT Department of Information Technology – Broadband Communication Networks (IBCN) MediaEval2012 Workshop, October 4-5, 2012, Pisa, Italy

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