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Machine annotation and retrieval for digital imagery of historical materials

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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 on the basis of 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 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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Author notes

  1. Jia Li

    Present address: Information Sciences and Technology Building, The Pennsylvania State University, University Park, PA, 16802, USA

Authors and Affiliations

  1. School of Information Sciences and Technology, The Pennsylvania State University, University Park, PA, 16802, USA

    James Z. Wang

  2. Lockheed Martin Corporation, Philadelphia, PA, USA

    Kurt Grieb

  3. Department of Electrical Engineering and Computer Science, The University of Kansas, Lawrence, KS, 66045, USA

    Ya Zhang

  4. Graduate School of Library and Information Science, Simmons College, Boston, MA, 02115, USA

    Ching-chih Chen

  5. Department of Computer Science, University of New Orleans, New Orleans, LA, 70148, USA

    Yixin Chen

  6. Department of Statistics, The Pennsylvania State University, University Park, PA, 16802, USA

    Jia Li

Authors
  1. James Z. Wang
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  2. Kurt Grieb
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  3. Ya Zhang
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  4. Ching-chih Chen
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  5. Yixin Chen
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  6. Jia Li
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Corresponding author

Correspondence to James Z. Wang.

Additional information

A preliminary version of this work has been presented at the DELOS-NSF Workshop on Multimedia in Digital Libraries, Crete, Greece, June 2003. The work was completed when Kurt Grieb and Ya Zhang were students of The Pennsylvania State University. James Z. Wang and Jia Li are also affiliated with Department of Computer Science and Engineering, The Pennsylvania State University. Yixin Chen is also with the Research Institute for Children, Children's Hospital, New Orleans.

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Wang, J.Z., Grieb, K., Zhang, Y. et al. Machine annotation and retrieval for digital imagery of historical materials. Int J Digit Libr 6, 18–29 (2006). https://doi.org/10.1007/s00799-005-0121-4

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  • DOI: https://doi.org/10.1007/s00799-005-0121-4

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