Machine Annotation and Retrieval for Digital Imagery
of Historical Materials
James Z. Wang (1), Kurt Grieb (2),
Ya Zhang (1),
Ching-chih Chen (3),
Yixin Chen (4),
Jia Li (1)
(1) The Pennsylvania State University
(2) Lockheed Martin Corporation
(3) Simmons College
(4) University of New Orleans
Abstract:
Annotating digital imagery of historical materials for the purpose of
computer-based retrieval is a labor-intensive task for many historians
and digital collection managers. We have explored the possibilities
of automated annotation and retrieval of images from collections of
art and cultural images. In this paper, we introduce the application
of the ALIP (Automatic Linguistic Indexing of Pictures) system,
developed at Penn State, to the problem of machine-assisted annotation
of images of historical materials. The ALIP system learns the
expertise of a human annotator based on a small collection of
annotated representative images. The learned knowledge about the
domain-specific concepts is stored as a dictionary of statistical
models in a computer-based knowledge base. When an un-annotated image
is presented to ALIP, the system computes the statistical likelihood
of the image resembling each of the learned statistical models and the
best concept is selected to annotate the image. Experimental results,
obtained using the Emperor image collection of the {\it Chinese Memory
Net} project, are reported and discussed. The system has been trained
using subsets of images and metadata from the Emperor collection.
Finally, we introduce an integration of wavelet-based annotation and
wavelet-based progressive displaying of very high resolution
copyright-protected images.
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Citation:
James Z. Wang, Kurt Grieb, Ya Zhang, Ching-chih Chen, Yixin Chen and
Jia Li, ``Machine Annotation and Retrieval for Digital Imagery of
Historical Materials,'' International Journal on Digital Libraries,
Special Issue on Multimedia Contents and Management in Digital
Libraries, vol. 6, no. 1, pp. 18-29, Springer-Verlag, 2006.
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Last Modified:
Thu Jan 20 18:27:26 EST 2005
© 2005, James Z. Wang