reliving on demand a total viewer experience

1

RELIVING ON DEMAND: A TOTAL VIEWER

EXPERIENCE

Vivek K. Singh1*, Jiebo Luo2, Dhiraj Joshi2, Phoury Lei2, Madirakshi Das2, Peter Stubler2

ACM International Conference on Multimedia – ACMM 2011

1 University of California, Irvine, 2 Kodak Research Laboratories, Rochester, NY,

* 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

TypeURL

Aesthetic IVI

dateTimesubjects

location

Height, width

Media

properties

Aesthetic

properties

Semantic

properties

Score Suitability

properties

Pre-processingMedia

Collection

Metadata

Repository

Date and Time

Extraction

Location Information

Extraction

Event Clustering

Aesthetics Value

Extraction

Face Detection

Face Clustering

Geographic

Clustering

Face Labeling

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

1,

trans.Y 1, scale 1)

Transform(θ2, trans.X

2,

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:\data\jiebo\cvpr2008\103_5972.jpg</pic>

<pic>c:\data\jiebo\cvpr2008\103_5973.jpg</pic>

<pic>c:\data\jiebo\lijiang-shangrila-day2\108_0043.jpg</pic>

<pic>c:\data\jiebo\lijiang-shangrila-day2\108_0044.jpg</pic>

</PicsShown>

</Snapshot>

</Interaction>

Evaluations

• Experiments with 11 families

• 35 user interaction sessions logged

• Roles

• 1st person (owner)

• 2nd person (immediate family)

• 3rd person (friends, cousins )

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

Experiment 1: Comparison with commercially available

options

6.2 Experiment 2: Use of different features across

different 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