Reliving on demand a total viewer experience

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.

Why do people take pictures? 1. Digital re-living 2. Sharing it with family and friends

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

Platforms Desktop Digital frame HDTV Kodak Gallery Mobile Kiosk

Preview • Re-living of events in user’s life, based on WHO, WHERE, and WHEN .

Outline • Preview • Design principles • System design • Under the hood (sneak peek) • Evaluations

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)

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.

System overview

System overview: Approach

Media data structure Media URL properties Type Height, width Aesthetic Aesthetic IVI properties location subjects dateTime Semantic properties Score Suitability properties

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

Reordering of event list• Basic idea• Time• People• Location

Choosing layout • Default:i= 2 3 4 5

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)

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

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

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>

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 )

Experiment 1: Comparison with commercially availableoptions

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

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

Reliving on demand a total viewer experience

by Vivek Singh

Accessibility

Categories

Upload Details

Usage Rights

Statistics

Reliving on demand a total viewer experience Presentation Transcript