1. Statistical techniques for video analysis and searching chapter 9 2010-11806 Anton Korotygin

2. Contents Introduction Model Vectors Video Search Fusion Experiments Conclusion

3. Before Starting What is the main approaches for video analysis based on vector model indexing and interactive search fusion ? Which technique we apply in this approaches ? What detectors we can use for that technique ?

4. Basic problems

5. Solution CBR – Content – based retrieval –searching and matching through the video based on similarity of its content MBR – Model – based retrieval – searching based on automatically extracted labels and detection results TBR – Text-based retrieval – applies to textual forms of information related to the video which includes transcripts, embedded, text, speech, metadata, etc…

6. How it works ?

7. Approaches Techniques Model Vector

8. Model Vectors Priori learning of detectors Concept detection and score mapping to produce model vectors

9. Priori learning of detectors

10. Concept learning Detection techniques –Support Vector Machines (SVM) –Gaussian Mixture Models (GMM) –Hidden Markov Models (HMM)

11. Concept detection

13. Support Vector Machines

14. Gaussian Mixture Models

16. Model Vector Construction

17. Model Vector Retrieval

18. Q&A

19. Thank you!!!