Statistical techniques for video analysis and searching chapter 9
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Transcript of Statistical techniques for video analysis and searching chapter 9
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Statistical techniques for video analysis and searching
chapter 9
2010-11806 Anton Korotygin
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Contents
• Introduction • Model Vectors • Video Search Fusion • Experiments • Conclusion
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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 ?
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Basic problems
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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…
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How it works ?
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Approaches Techniques
• Model Vector
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Model Vectors
• Priori learning of detectors • Concept detection and score map-
ping to produce model vectors
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Priori learning of detectors
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Concept learning
• Detection techniques – Support Vector Machines (SVM)– Gaussian Mixture Models (GMM)– Hidden Markov Models (HMM)
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Concept detection
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Support Vector Machines
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Gaussian Mixture Models
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Model Vector Construction
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Model Vector Retrieval
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Q&A
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Thank you!!!