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New Software Creates Dictionary for Retrieving Images
software that responds to written questions by retrieving digital
images has potentially broad application, ranging from helping
radiologists compare mammograms to streamlining museum curators'
archiving of artwork, say the Penn State researchers who developed the
Dr. James Z. Wang,
assistant professor in Penn State's School of Information Sciences and
Technology and principal investigator, says the Automatic Linguistic
Indexing of Pictures (ALIP) system first builds a pictorial dictionary,
and then uses it for associating images with keywords. The new
technology functions like a human expert who annotates or classifies
"While the prototype is in its infancy, it has
demonstrated great potential for use in biomedicine by reading x-rays
and CT scans as well as in digital libraries, business, Web searches
and the military," said Wang, who holds the PNC Technologies Career
Development Professorship at IST and also is a member of the Department
of Computer Science and Engineering.
The system is detailed in a
paper, "Learning-based Linguistic Indexing of Pictures with 2-D MHMMs,"
to be given today (Dec. 4) at the Association of Computing Machinery's
(ACM) Multimedia Conference in Juan Les Pins, France. Co-author is Dr.
Jia Li, Penn State assistant professor of statistics.
other content-based retrieval systems that compare features of visually
similar images, ALIP uses verbal cues that range from simple concepts
such as "flowers" and "mushrooms" to higher-level ones such as "rural"
and "European." ALIP also can classify images into a larger number of
categories than other systems, thereby broadening the uses of image databases.
advantages include ALIP's abilities to be trained with a relatively
large number of concepts simultaneously and with images that are not
necessarily visually similar.
Wang and Li are using ALIP as part
of a three-year National Science Foundation research project to develop
digital imagery technologies for the preservation and cataloguing of
Asian art and cultural heritages. This research aims to bypass or
reduce the efforts in the labor-intensive creation and entry of manual
descriptions or artwork.
Eventually, the system is expected to
identify the discriminating features of Chinese landscape paintings and
the distinguishing characteristics of paintings from different
historical periods, Wang notes.
The researchers' progress in the
first year of that project is discussed in the paper,
"Interdisciplinary Research to Advance Digital Imagery Indexing and
Retrieval Technologies for Asian Art and Cultural Heritages." The
research will be presented on Dec. 6 at in a special session of ACM's
Multimedia Conference in France.
"This system has the potential
to change how we handle images in our daily life by giving us better
and more access," Wang says. Wang and Li's latest research builds on
their earlier efforts at Stanford University. Sun Microsystems provided
most of the equipment used in the project.
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