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Tackling the digital video overload

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Tackling the digital video overload.

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Tackling the digital video overload

  1. 1. Tackling the Digital Video Overload Wesley De Neve8/11/2012 1
  2. 2. Context (1/2) Increasing consumption of online video content  easy-to-use devices and online services  cheap storage and bandwidth  more and more people going online Increasing availability of online video content  digitization of professional video archives  popularity of user-generated video content 8/11/2012 2
  3. 3. Context (2/2) Some statistics  professional video content  BBC Motion Gallery (as of January 2009)  offers over 2.5 million hours of video content  with video content dating back 60 years in time  user-generated video content  YouTube (as of October 2012)  people watch 4 billion hours of video content each month  people upload 72 hours of video content each minute 8/11/2012 3
  4. 4. Digital Video Overload (1/2) Problem description  our ability to manage video content is not able to keep up with our ability to create video content Cause  to facilitate text-based video search, we need to manually annotate video content with textual labels 8/11/2012 4
  5. 5. Digital Video Overload (2/2) Real cause  people experience manual video annotation as time- consuming and cumbersome, thus foregoing the effort Solution  automatic video content understanding  this is, computerized translation of pixels into text “Curiosity on Mars” 8/11/2012 5
  6. 6. Automatic Video Content Understanding Traditionally: video content analysis  works reasonably well in highly controlled environments  room for improvement in terms of applicability and effectiveness Nowadays: video content analysis, enhanced with  unstructured knowledge from the Social Web, and/or  structured knowledge from the Semantic Web two use cases 8/11/2012 6
  7. 7. Social Video Face Annotation (1/2) Description  improving face annotation for personal video collections by harvesting online social network context Goal of video face annotation person 2 person 1 person 3 Search for peoples 8/11/2012 7
  8. 8. Social Video Face Annotation (2/2) Contact list Labeled face images contact 1 contact 2 occurrence contact 3 + probabilities contact 4 contact 5 co-occurrence contact 6 probabilities video face recognition using visual features robust video face recognition using visual and social features 8/11/2012 [ published in IEEE ToMM, 2011 ] 8
  9. 9. Annotation of Live Soccer Video (1/2) Description  annotation of live soccer video by harvesting collective knowledge from Twitter Goal of annotating soccer video logo attack goal trainer logo Search for events 8/11/2012 9
  10. 10. Annotation of Live Soccer Video (2/2) 6 Tweets/s 4 2 0 0 5 Time (s) 10 soccer event detection using visual features Twitter-assisted annotation What is happening? of live soccer video What are people saying? 8/11/2012 [ submitted to IEEE ToMM, 2012 ] 10
  11. 11. Other Use Cases Movie actor recognition Semantic video copy detection Audiovisual enrichment of text documents 8/11/2012 11
  12. 12. Research Challenges (1/2) Design of techniques that jointly take advantage of unstructured and structured knowledge  unstructured knowledge: collective knowledge  structured knowledge: Linked Data Cloud  cf. “Everything is Connected” for video content enrichment  http://everythingisconnected.be/ Design of techniques for translating unstructured knowledge into structured knowledge  velocity, volume, and variety  sparsity, ambiguity, and complexity 8/11/2012 12
  13. 13. Research Challenges (2/2) Design of effective semantic similarity metrics visual distance semantic distance Design of user-oriented performance metrics  need to go beyond the use of precision and recall  need to better capture whether the needs of users have been met by a video content retrieval system 8/11/2012 13
  14. 14. Thank you! 8/11/2012 14

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