Content-Based Video Analysis

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Talk at VIPP 2010 Symposium, Jan 12th 2010.

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Content-Based Video Analysis

  1. 1. VIPP Symposium, January 12, 2010 Content-Based Video Analysis Markus Koskela
  2. 2. Background Aalto University School of Science and Technology Department of Information and Computer Science (http://ics.hut.fi/ ) Adaptive Informatics Research Centre, Academy CoE (http://www.cis.hut.fi/research/ ) Erkki Jorma Ville Mats He Xi Jing Oja Laaksonen Viitaniemi Sjoberg ¨ Zhang Chen Wu (2/20)
  3. 3. Overview of my talk • Introduction • Concept-based video search • Video summarization • Video analysis in AR applications (3/20)
  4. 4. Content-based analysis of video • Visual features • Audio features, speech, text, . . . (4/20)
  5. 5. Indexing with low-level features (5/20)
  6. 6. Content-based image retrieval (6/20)
  7. 7. Google Images (7/20)
  8. 8. Types of video material Shot-based video, obtained with shot boundary detection Continuous material, sampled with regular interval Online video, received one frame at a time (8/20)
  9. 9. Shot-based video indexing video shot ...missile Motion SOM launch... keyframe audio text Temporal Color SOM binary keyword Texture SOM Color SOM Audio SOM Word histogram Keyword freq. inverted file (9/20)
  10. 10. Concept-based video search (10/20)
  11. 11. (11/20)
  12. 12. Concept ontologies sports weather court office meeting studio outdoor building desert vegetation mountain road sky snow urban waterscape/ crowd face waterfront person police/ military prisoner animal computer/TV US flag airplane car security screen bus truck boat/ship walking/ people explosion/ natural maps charts running marching fire disaster (12/20)
  13. 13. Concept-based video search (13/20)
  14. 14. Video summarization (14/20)
  15. 15. Summarization pipeline (15/20)
  16. 16. Video analysis in AR applications (16/20)
  17. 17. Contextual IR in AR applications (17/20)
  18. 18. Devices for mobile AR (18/20)
  19. 19. Video analysis for AR (19/20)
  20. 20. Thank you! (20/20)

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