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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