Statistical techniques for video analysis and searching

chapter 9

2010-11806 Anton Korotygin

Contents

• Introduction • Model Vectors • Video Search Fusion • Experiments • Conclusion

Before Starting

• What is the main approaches for video analysis based on vector model indexing and interactive search fu-sion ?

• Which technique we apply in this ap-proaches ?

• What detectors we can use for that technique ?

Basic problems

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 tex-tual forms of information related to the video which includes transcripts, embed-ded, text, speech, metadata, etc…

How it works ?

Approaches Techniques

• Model Vector

Model Vectors

• Priori learning of detectors • Concept detection and score map-

ping to produce model vectors

Priori learning of detectors

Concept learning

• Detection techniques – Support Vector Machines (SVM)– Gaussian Mixture Models (GMM)– Hidden Markov Models (HMM)

Concept detection

Support Vector Machines

Gaussian Mixture Models

Model Vector Construction

Model Vector Retrieval

Q&A

Thank you!!!