AutoAlbum:
A System for Browsing Through Personal Digital Photographs
Contacts
John
Platt, Erin Renshaw
Quick
Links
About AutoAlbum & PhotoTOC
It is getting
increasingly popular for consumers to buy a digital camera and take thousands
of photos of daily life. Most consumers simply dump these photos into one
directory, analogous to dumping developed prints into a shoebox. A typical user
generates thousands of photos a year. Finding a photo in this shoebox directory
is difficult.
AutoAlbum and
PhotoTOC are browsing user interfaces that help solve this problem. AutoAlbum
was the original UI, while PhotoTOC is a new, updated UI. PhotoTOC consists of
two panes. Thumbnails of all images in the shoebox directory
is shown on the right pane, as a big contact sheet. PhotoTOC
automatically clusters these images. One representative photograph from every
cluster is shown on the left pane. When the user clicks on a representative
photograph, the right pane scrolls to show that same photograph in the center
of the window. The user can then find his/her photograph with minimal scrolling
on the right-hand pane.
Both AutoAlbum
and PhotoTOC use two forms of metadata to help cluster the photos: the creation
time of the photo and the order that the photos were taken. Under some
circumstances, the creation time of the photo is preserved after download from
the camera to the PC. In those cases, PhotoTOC can cluster on the creation time
and ignore the content. In other cases, the creation time is destroyed (e.g.,
by downloading with a serial cable, or camera running out of batteries).
However, the order of the photographs is still often preserved, via either
download time or file name. In this second case, PhotoTOC uses the content of
the photos to cluster, but creates clusters that obey the photographic order.
In the demo,
the combination of these two clustering techniques are used: first, time-based
clustering uses the creation date of the file to form albums. If the creation
date of the file isnt the creation date of the photo, time-based clustering
will produce very large clusters. So, any very large cluster will get broken
down by content-based clustering that obeys the order of the photographs.
Brent
Field, Mary Czerwinski, and I have
done a user study of AutoAlbum and PhotoTOC. We started with a pilot
user study of AutoAlbum. Using the feedback about AutoAlbum, we
designed PhotoTOC and ran a full study. PhotoTOC had higher user
satisfaction than both AutoAlbum and the Windows XP Shell browser set
to thumbnail view. You can read about the re-design and the
study in a
technical report. The original AutoAlbum clustering algorithm is
described in the paper: John C. Platt, AutoAlbum:
Clustering Digital Photographs Using Probabistic Model
Merging, in Proc. IEEE Workshop on Content-Based Access of
Image and Video Libraries 2000, to appear,
(2000).
You can look
at how PhotoTOC works on my photographs through a DHTML demo.
This
page was written by John Platt of the CCSP Group of Microsoft Research. Last
updated:
09/17/2002
