Machine Annotation for Digital Imagery of Historical Materials
using the ALIP System
James Z. Wang, Jia Li
The Pennsylvania State University, University Park, PA 16802
Simmons College, Boston, MA 02155
Manual annotating digital imagery of historical materials is a
labor-intensive task for many historians. In this paper, we introduce
the application of the ALIP (Automatic Linguistic Indexing of
Pictures) system, developed at The Pennsylvania State University, to
the problem of machine assisted annotation of these images. The ALIP
system learns the expertise of a human annotator from a small
collection of 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 a new image is
presented to ALIP, the system computes the statistical likelihood of
the image resembling each of the learned statistical models and the
best few are further studied for the purpose of annotating the image
with keywords. The Penn State research team applied their ALIP system
to the EMPEROR images and metadata created by C.-c. Chen. Promising
results have been obtained.
Full Paper in Color
James Z. Wang, Jia Li and Ching-chih Chen, ``Machine Annotation for
Digital Imagery of Historical Materials Using the ALIP System,''
Proc. DELOS-NSF Workshop on Multimedia in Digital Libraries, 5 pages,
Crete, Greece, 2003.
Copyright 2003 .
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May 4, 2003