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Taxonomic classification for web-based videos

Taxonomic classification for web-based videos. Author: Yang Song et al. (Google) Presenters: Phuc Bui & Rahul Dhamecha. 1. Introduction. Taxonomic classification for web-based videos. Web-based Video Classification. Web-based Video (e.g. Youtube )

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Taxonomic classification for web-based videos

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  1. Taxonomic classification for web-based videos Author: Yang Song et al. (Google) Presenters: Phuc Bui & Rahul Dhamecha

  2. 1. Introduction

  3. Taxonomic classification for web-based videos

  4. Web-based Video Classification • Web-based Video (e.g. Youtube) • Over 800 million unique users visit / month • Over 4 billion hours of video are watched / month • 72 hours of video are uploaded / minute • Classification • Improve User experience • Increase Website profit

  5. What’s interesting? • Large-scale classification • Taxonomy of categories • Unlimited domain • Combined Approach • Text • Labeled Web documents • Labeled Video • Video • Content-based features

  6. Overview Approach • Multi-labels Classification • One classifier for each category • Classifiers • Text-based Classifier • from Web-based Documents • Combined Classifier • Text-based Classifier • Video content-based features

  7. 2. Algorithms

  8. TAXONOMIC CLASSIFICATION: - THE VARIOUS CATEGORIES.

  9. TRAINING SET OF EACH CATEGORY

  10. MIGRATION FROM TEXT TO VIDEO Pre-trained text based classifiers of each category used for porting videos Labeled Video data is used for training these classifiers No. of Classifiers = No. of Categories Ada-boosting is deployed to aggregate these weak classifiers to a Strong Classifier

  11. Feature Extraction Text Based Features. Title Description Keywords President Obama: the Real Mitt Romney - Denver, Colorado

  12. Content Based Features Moments from multi-scale analysis Color Histogram Mean, variance of each channel. Difference between mean of center and boundary

  13. Content Based Features contd… Edge Detection Canny Edge Detection Algorithm

  14. Content Based Features contd… Color MotionFeatures Cosine Difference of the histograms of subsequent frames.

  15. Content Based Features contd… Shot Boundary Features Types Hard Cut Fade Dissolve Wipe

  16. Hard Cut  instantaneous transition from one scene to the next

  17. Fade A Fade which is a gradual between a scene and a constant image (fade-out) or between a constant image and a scene (fade-in).

  18. Dissolve A Dissolve is a gradual transition from one scene to another in which the first scene fade-out and the second scene fade-in. so it is a combination of fade-in and fade-out.

  19. Wipe A Wipe is a gradual transition in which a line move across the screen, with the new scene appearing behind the line.

  20. Integration

  21. 3. Experiments

  22. Data • 5789 videos • 9087 labels • 565 categories • 80% training • 20% evaluation

  23. Evaluation • Precision • Recall • F-score

  24. Results • Sample videos

  25. Results • 80-category classifiers • 1037-category classifiers

  26. Results

  27. Results • Adaption + Content-based features classifiers • Content-based features-only classifiers

  28. 4. Conclusion & Dicussion • Video features • Content-based • Associated texts • Web-documents based text classifier • Semi-supervised learning • Image-based classifiers • ImageNet

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