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Reliving on demand a total viewer experience

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Enabling
media reliving experiences that are aesthetically pleasing,
interactive, and semantically drivable as they center on people,
locations, time, and events discovered in a media collection.

Published in: Technology, Art & Photos
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Reliving on demand a total viewer experience

  1. 1. RELIVING ON DEMAND: A TOTAL VIEWEREXPERIENCE Vivek K. Singh1*, Jiebo Luo2, Dhiraj Joshi2, Phoury Lei2, Madirakshi Das2, Peter Stubler2 1 University of California, Irvine, 2 Kodak Research Laboratories, Rochester, NY, ACM International Conference on Multimedia – ACMM 20111 * Work was done when the author was interning at Kodak Research Laboratories, Eastman Kodak Company, Rochester, NY, USA.
  2. 2. Why do people take pictures? 1. Digital re-living 2. Sharing it with family and friends
  3. 3. What’s available today?• Commercial Slideshows (Picasa, iPhoto, ACDsee): • Focus on visual appearance only. • Don’t understand/utilize semantics (except “FaceMovie”)• Research efforts: Semantic analysis • No interaction • Interaction on demand• Allow different users to dynamically re-direct the flow of media reliving experience
  4. 4. Platforms Desktop Digital frame HDTV Kodak Gallery Mobile Kiosk
  5. 5. Preview • Re-living of events in user’s life, based on WHO, WHERE, and WHEN .
  6. 6. Outline • Preview • Design principles • System design • Under the hood (sneak peek) • Evaluations
  7. 7. Design principles 1. User controllable: • Responsive to user demand (overcoming intent gap) 2. Semantically drivable: • Events as organizing units • Who, when, where; what 3. Aesthetically pleasing: • Dynamic presentation • Multimodal (songs, images, videos)
  8. 8. Retrieval vs. Browsing vs. Reliving• Media by itself is uninteresting unless it performs a function (e.g. reliving, sharing) for the human user• Retrieval • Fetching data. Strong intent (e.g. search)• Browsing • Piecemeal reliving. Weak intent (e.g. youtube)• Reliving • Valuable middle ground. • Semantically re-direct the flow if desired.
  9. 9. System overview
  10. 10. System overview: Approach
  11. 11. Media data structure Media URL properties Type Height, width Aesthetic Aesthetic IVI properties location subjects dateTime Semantic properties Score Suitability properties
  12. 12. Pre-processing Media Collection Date and Time Aesthetics Value Face Detection Location Information Extraction Extraction Extraction Face Clustering Event Clustering Face Labeling Geographic Clustering Metadata Repository
  13. 13. Reordering of event list• Basic idea• Time• People• Location
  14. 14. Choosing layout • Default:i= 2 3 4 5
  15. 15. Choose transitions• If (criteria=time || criteria=loc) • Slide In/Out• If (criteria=personi) • Face2Face transition Transform(θ1, trans.X Transform(θ2, trans.X 1, 2, trans.Y 1, scale 1) trans.Y2, scale 2)
  16. 16. Choose song• If (criteria=time) • Select seasonal songs (easily extensible to finer grain)• If (criteria=loc) • Select regional songs• If (criteria=personi) • Select age-based songs (easily extensible to gender)• Taken from a library of available songs
  17. 17. Show images • In time order • Higher score => more display time • Auto-zoom-crop • Find center to focus on • Match the aspect ratio required • Multiple Holes in transitions • Token passing amongst holes • Representative image as background
  18. 18. Logging user sessions <Interaction> <Click> <GlobalEventID>urn:guid:f1337996-3c28-4345-b4fb-c4f1b788fc05</GlobalEventID> <SortedEventID>0</SortedEventID <TimeStamp>10:17:47 AM</TimeStamp> <Criteria_type>gps</Criteria_type> <Criteria_value>61.2175937710438 , -149.898739309764</Criteria_value> <HotSpotClick>False</HotSpotClick> </Click> <Snapshot> <Locations> <loc>-149.898739309764,61.2175937710438</loc> <loc>-73.508556462585,40.5956603174603</loc> <loc>102.757525301205,25.1018832329317</loc> <loc>104.195397,35.86166</loc> <loc>6.09306585111111,52.7236709366667</loc> </Locations> <People> <peo>Jiebo</peo> <peo>Joyce</peo> <peo>Xinping</peo> <peo></peo> <peo></peo> </People> <SortedEvents> <eve>urn:guid:f1337996-3c28-4345-b4fb-c4f1b788fc05</eve> <eve>urn:guid:f1337996-3c28-4345-b4fb-c4f1b788fc05</eve> <eve>urn:guid:f1337996-3c28-4345-b4fb-c4f1b788fc05</eve> <eve>urn:guid:f1337996-3c28-4345-b4fb-c4f1b788fc05</eve> <eve>urn:guid:f1337996-3c28-4345-b4fb-c4f1b788fc05</eve> <eve></eve> </SortedEvents> <PicsShown> <pic>c:datajiebocvpr2008103_5972.jpg</pic> <pic>c:datajiebocvpr2008103_5973.jpg</pic> <pic>c:datajiebolijiang-shangrila-day2108_0043.jpg</pic> <pic>c:datajiebolijiang-shangrila-day2108_0044.jpg</pic> </PicsShown> </Snapshot> </Interaction>
  19. 19. Evaluations• Experiments with 11 families• 35 user interaction sessions logged Age of contributing photographers 23 to 56 No. of images/ videos in the collection 2,091 to 10,522 No. of calendar years in time span 3 to 10 No. of tagged people in the collection 26 to 137 No. of places in the collection 19 to 45• Roles • 1st person (owner) • 2nd person (immediate family) • 3rd person (friends, cousins )
  20. 20. Experiment 1: Comparison with commercially availableoptions
  21. 21. 6.2 Experiment 2: Use of different features acrossdifferent user demographics Females 1.14 1.49 1.13 1.01 Males 1.41 1.25 2.08 1.43 Both 1.30 1.27 1.28 1.35 All 1st party 2nd party 3rd party Active Vs Passive? Clicks per axis Stickiness :Time spent after clicks
  22. 22. Future work • Choosing songs more generically/smartly • Choosing optimal spatio-temporal placement of images in the slide show • Choosing layout • Choosing transition time? • Supporting multiple axes simultaneously • Previews

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