Talk at VIPP 2010 Symposium, Jan 12th 2010.

1. VIPP Symposium, January 12, 2010
Content-Based Video Analysis
Markus Koskela

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. Overview of my talk
• Introduction
• Concept-based video search
• Video summarization
• Video analysis in AR applications
(3/20)

4. Content-based analysis of video
• Visual features
• Audio features,
speech, text, . . .
(4/20)

5. Indexing with low-level features
(5/20)

6. Content-based image retrieval
(6/20)

7. Google Images
(7/20)

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. 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. Concept-based video search
(10/20)

11. (11/20)

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. Concept-based video search
(13/20)

14. Video summarization
(14/20)

15. Summarization pipeline
(15/20)

16. Video analysis in AR applications
(16/20)

17. Contextual IR in AR applications
(17/20)

18. Devices for mobile AR
(18/20)

19. Video analysis for AR
(19/20)

20. Thank you!
(20/20)