Mor Naaman, Yee Jiun Song, Andreas Paepcke, Hector Garcia-Molina Digital Library Project, Database...

Mor Naaman, Yee Jiun Song, Andreas Paepcke, Hector Garcia-Molina

Digital Library Project, Database Group

Stanford University

Automatic Organization for Digital Photographs with Geographic Coordinates

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Geo-Referenced Photos

April 8th, 2004 1:20:02pm

Latitude: N34.3121

Longitude: W122.234

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Geo-Photography Technology

+1) 2)

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Personal Photo Libraries

• Searching/browsing very difficult

• Little discernible structure to photo collections

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• Content-based retrieval– Basic, primitive (far from semantic)

• Manual labeling– Improved, yet cumbersome

• Visual methods for fast scanning (Zoom)– Don’t scale well

Managing Personal Photos

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Our Approach

• Absolutely no human effort required

• Utilize time and location– Automatically captured – Easy to get

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Automatic Organization

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Automatic Organization

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Automatic Organization

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Automatic Organization

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Outline

• Requirements and challenges

• The algorithms

• Sample output

• Experiment results

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Browsing by Location/Time

• Use a map/calendar– wwmx.org from MSR:

• Map issues – Lots of screen space– Sparse – Limited interaction?– Not intuitive for some

Using Hierarchies

Time

United States

Yosemite N.P, Yosemite Valley, CA

Location:Around: San Francisco, Berkeley, Sonoma CA

San Francisco, Golden Gate Park, CA

Seattle, WA

…

……

…

Berkeley,

Oakland CA

2003-01-01: Yosemite N.P. (2 Days)

2003-01-18: San Francisco (1 hour)

2003-01-18: San Francisco (1 hour)Time:

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Challenges

• Locations should be intuitive

• Events are tricky – 3-days trip to NYC– The kid’s soccer game, followed by a

birthday party

• Good names are important.

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Outline

• Requirements and challenges

• The algorithms

• Sample output

• Experiment results

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Process Diagram

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Discovering Structure

Location Hierarchy

Initial Event Segmentation

Location Clustering

Final Event Segmentation

Event Hierarchy

Initial Event Segmentation

Automatic Organization

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Initial Event Segmentation

• Photos occur in bursts

• Identify bursts: semantically “connected”

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Initial Event Segmentation

Stream of photos

More details: •Graham et al, JCDL 2002•Tomorrow•Proceedings

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Discovering Structure

Location Hierarchy

Initial Event Segmentation

Final Event Segmentation

Event Hierarchy

Location Clustering

Automatic Organization

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Location Clusters

• Cluster the bursts into locations

• A. Gionis and H. Mannila. Finding recurrent sources in sequences. In Proceedings, Computational molecular biology 2003.– Minimize: number of clusters– Minimize: error (distance to cluster centers)

Photo location

Location Clusters: 2-D View

2-D View: with Bursts

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Location Clusters

Location4 -

Location3 -

Location2 -

Location1 -

Location4 -

Location3 -

Location2 -

Location1 -

Location Clusters (breakdown)

• Some clusters may be overloaded:– Many bursts / picture-taking days in one location

San Francisco

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Discovering Structure

Location Hierarchy

Initial Event Segmentation

Location Clustering

Event Hierarchy

Final Event Segmentation

Automatic Organization

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Final Event Segmentation

• Again scan sequence, new events detected:– Whenever location context changes– In the same location, use adaptive time

threshold

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Final Event Segmentation

Overnight trip to Yosemite

Soccer game and dinner

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Next - names

• Detected location and event structure

• Need to choose names for each node

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Assigning Names

Photo location

Stanford

Palo Alto City Park

Palo AltoButano State Park

Stanford 42

Palo Alto 30

Butano 10

P.A. park 8

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Assigning Names – Nearby?

San Jose, 20 miles

San Francisco, 30 milesWhat if photos occur sparsely within cities or parks?

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Assigning Names - Nearby

Which city has stronger “gravity”?

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Assigning Names - Nearby

San Jose is Closer

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Assigning Names - Nearby

San Jose is bigger**larger population

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Assigning Names - Nearby

But San Fran is more important!**greater Google count

Final name for location cluster:

“Stanford, 30 miles South of SF”

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Assigning Names - Alexandria

• Using polygon-based dataset of administrative areas

• Alexandria gazetteer can be used for other prominent geographic features

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Outline

• The requirement and challenges of automatic organization

• The algorithms

• Sample output

• Experiment results

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Location Hierarchy

Photoshop Album (at least 4 man-hours)

Our system (about 0 man-seconds)

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Location Hierarchy (US)

+San Francisco, Berkeley, Sonoma, CA-Stanford, Mountain View, Monterey, CA

•Monterey (58 miles S of San Jose) •Mountain View (4 miles NW of San Jose) •Stanford

-Colorado (219 miles W of Denver)-Long Beach (35 miles S of Los Angeles, CA)-Philadelphia, PA-Seattle, WA-Sequoia N.P. (153 miles E of Fresno, CA)-South lake Tahoe; Bear Valley, CA-Yosemite N.P.; Yosemite Valley, CA 

Events

about 0 man-seconds:

...

2003-06-28: Long Beach,CA (3 days)

2003-07-04: San Francisco,CA (3 hours)

2003-07-10: Colorado (3 days)

2003-07-15: San Francisco,CA(1 hours)

2003-07-18: Mountain View,CA (5 hours)

2003-07-27: San Francisco,CA (1 hours)

2003-09-28: Philadelphia,PA (1 hours)

2003-10-03: Sequoia NP (3 days)

...

Photoshop Album (at least 4 man-hours)

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Event Names

• LOCALE: share automatically

• Check personal calendar

• Event Gazetteer

• Easy interface

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Experiment

• Tested on 3 real-world geo-referenced photo collections

• Our system automatically generated the structure and names

• Tested with the owners

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Experiment - Locations

• Accepted the automatic hierarchy

• Only minor edits requested– Merge/split few of the locations

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Experiment - Events

• Compared to events as annotated by users

• 80-85% in both recall and precision

• Other metrics proposed (see paper)

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Experiment - Naming

• Naming location clusters– For 76% of clusters, system and users pick

at least one name in common– For the rest, “automatic” name was useful

Not yet published:

• Paid 13 participants to “geo-reference” their photos• Loaded to WWMX and our browser

– Most liked the map better, but…– Performed the same for search/browse tasks– Event notion helps overcome location handicap– Organization “made sense”

P.S. Some didn’t touch the map, yet used our location hierarchy.

P.S.2 This was on a BIG screen!

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Thank You!

More details:

Proceedings

Google: Mor Naaman

mor@cs.stanford.edu

http://www-db.stanford.edu/~mor/

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Future Work

• User interface

• PDA

• Integrate with map

• Global photo libraries

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Remember The Bursts?