1. Content-based Lecture Video Indexing Martin Halvorsen

2. Content-based Lecture Video Indexing Content Introduction Research Questions Background Proposed System Implementations Conclusions

3. Content-based Lecture Video Indexing IntroductionIntroduction

4. Introduction Blackboard presentations considered as essential and indispensable Hard to navigate through traditional videos Lack of expertise and time consuming

5. Content-based Lecture Video Indexing Research questions

6. Content-based Lecture Video Indexing Research questions Q1: How can foreground/background segmentation in a lecture video work for different writing-boards?

7. Content-based Lecture Video Indexing Research questions Q2: How to automatically extract meta-data from lecture videos?

8. Content-based Lecture Video Indexing Research questions Q3: How to use such meta-data for indexing and searching of lecture videos?

9. Content-based Lecture Video Indexing BackgroundBackground

10. Background Tracking, detection and separation algortihms Motion estimation, background models, statistical estimated models

11. Content-based Lecture Video Indexing Background Eirik Grythe – segmentation and text detection Synne Repp – simulated indexing alg.

12. Content-based Lecture Video Indexing Proposed System

13. Content-based Lecture Video Indexing Proposed system Superior blockdiagram of the proposed system Blackboard extractionMetadata ExtractionIndex GenerationF/B Segmentation

14. Content-based Lecture Video Indexing Teacher segmentation

15. Content-based Lecture Video Indexing Teacher segmentation Motion estimation - SAD

16. Content-based Lecture Video Indexing Teacher segmentation Morphological operations to close holes in the detection

17. Content-based Lecture Video Indexing Teacher segmentation Using motion history to improve the segmentation algorithm

18. Content-based Lecture Video Indexing Teacher segmentation Replace blocks that has motion in it

19. Content-based Lecture Video Indexing Teacher segmentation Demonstration

20. Content-based Lecture Video Indexing Meta-data extraction

21. Content-based Lecture Video Indexing Meta-data extraction What is meta-data? How to extract blackboard content? Statistics

22. Content-based Lecture Video Indexing Meta-data extraction Demonstration

23. Content-based Lecture Video Indexing IndexingIndexing

24. Indexing When to extract an image? Search a lecture video using extracted features

25. Content-based Lecture Video Indexing ConclusionsConclusions

26. Conclusions Adaptive segmentation possible by using motion estimation Extraction of content is possible using image difference Meta-data is possible to extract using a foreground model to compare against new content Statistics of extracted meta-data can be used to automatically index a lecture video

27. Content-based Lecture Video Indexing The end