Creating a large-scale content-based airphoto image digital library

Bin Zhu, Marshall Ramsey, Hsinchun Chen

Research output: Contribution to journalArticlepeer-review

39 Scopus citations


This paper describes a content-based image retrieval digital library that supports geographical image retrieval over a testbed of 800 aerial photographs, each 25 megabytes in size. In addition, this paper also introduces a methodology to evaluate the performance of the algorithms in the prototype system. The major contributions of this paper are two. 1) We suggest an approach that incorporates various image processing techniques including Gabor filters, image enhancement, and image compression, as well as information analysis technique such as self-organizing map (SOM) into an effective large-scale geographical image retrieval system. 2) We present two experiments that evaluate the performance of the Gabor-filter-extracted features along with the corresponding similarity measure against that of human perception, addressing the lack of studies in assessing the consistency between an image representation algorithm or an image categorization method and human mental model.

Original languageEnglish (US)
Pages (from-to)163-167
Number of pages5
JournalIEEE Transactions on Image Processing
Issue number1
StatePublished - Jan 2000

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


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