CVPR 2001 Short Course
Image Search Engines: Techniques and
Applications
Instructors:
Theo Gevers (
MOTIVATION
Very
large digital image archives have been created and used in a number of
applications including archives of images of postal stamps, textile patterns,
museum objects, trademarks and logos, and views from everyday life as it
appears in home videos and consumer photography. Moreover, with the growth and
popularity of the World Wide Web, a tremendous amount of visual information is
made accessible publicly. As a consequence, there is a growing demand for
search methods retrieving pictorial entities from large image archives. In this
tutorial we will give a survey of the most recent developments on image search
engines focusing on the following topics:
TOPICS
Color,
Texture and Shape Features:
Color provides
powerful information for image retrieval. A color survey is given consisting of
an introduction to standard color models as well as an overview of best recent
work on color invariance. Further, we analyze in detail which color models to
use under which imaging parameters. This is useful for image retrieval
applications where no constraints on the imaging process can be imposed as well
as for applications where one or more parameters of the imaging process can be
controlled. Further, an extensive overview is given of current shape features
for image retrieval.
Searching:
In the
field of pattern recognition, several methods have been proposed that improve
classification automatically through experience such as artificial neural
networks, decision tree learning, Bayesian learning and k-nearest neighbor
classifiers. A survey is given on the most recent developments regarding the
use of these classification techniques for image classification and retrieval.
Indexing:
Various
methods have been developed for indexing the stored images so that the image
retrieval methods can perform efficiently at some additional costs in memory,
such as a k-d tree, R-tree or a X-tree, for example. A survey is given of
indexing methods in the context of image retrieval.
Visualization:
Visualization
of the feature matching results is very important and will be addressed in
detail. Further, methods are discussed to localize object in images.
Relevance
feedback:
From
the user feed-back giving negative/positive answers, methods can automatically
learn which image features are more important. Methods and systems are
discussed using relevance feedback for image retrieval.
Survey
of image retrieval system:
Finally,
a survey of existing image retrieval systems is given. The systems are compared
with respect to above discussed topics.
TELEPHONE,
FAX NUMBERS AND EMAIL ADDRESSES:
Dr.
Theo Gevers and Prof. Arnold W.M. Smeulders
University
of Amsterdam
Faculty
of Science
Intelligent
Sensory Information Systems
Kruislaan
403
1098 SJ
Amsterdam
tel.
020-5257463
fax.
020-5257490
gevers@wins.uva.nl
smeulders@wins.uva.nl
BRIEF
BIOGRAPHIES OF BOTH AUTHORS:
Theo
Gevers is assistant professor of Computer Science at the University of
Amsterdam, The Netherlands. His main research interests are in the fundamentals
of image database system design, image retrieval by content, theoretical
foundation of geometric and photometric invariants and color image processing.
He has led several (inter)national projects and acts as a reviewer. He is co-organizer
of the First International Workshop on Image Databases and Multi Media Search,
the Third International Conference on Visual Information Systems and the First
Dutch Workshop on Colour Imaging and Vision. He has published over 50 papers on
color image processing, image retrieval and image database design.
Arnold
W.M. Smeulders is professor of Computer Science in Multi Media Information
Analysis. He has been in computer vision since 1975. He has published some 200
papers and an equal amount of conference contributions mostly on vision and
recognition, with a new emphasis on multimedia analysis. He heads the
Intelligent Sensory Information Systems group at the University of Amsterdam.
His current research interest is in industrial vision from specification,
colour vision, image search by pictorial example and image databases,
intelligent interactive analysis, and system design aspects of multimedia
systems, but he is mostly intrigued by the correspondence between language and
picture. He is co-chair of IAPR's TC12 on Multi-Media, associate editor for
IEEE-transactions PAMI and Cytometry, and a member of the Visual Information
Systems steering committee. He is also director of the Research Institute
Computer Science and department head of the University of Amsterdam, and
director of the Intelligent Systems Lab Amsterdam.
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