Statistical techniques for video analysis and searching chapter 9

1. Statistical techniques for video analysis and searchingchapter 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!!!