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Proceedings Paper

Retrieval of multi- and hyperspectral images using an interactive relevance feedback form of content-based image retrieval
Author(s): Irwin E. Alber; Morton S. Farber; Nancy Yeager; Ziyou Xiong; William M. Pottenger
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Paper Abstract

This paper demonstrates the capability of a set of image search algorithms and display tools to search large databases for multi- and hyperspectral image cubes most closely matching a particular query cube. An interactive search and analysis tool is presented and tested based on a relevance feedback approach that uses the human-in-the-loop to enhance a content-based image retrieval process to rapidly find the desired set of image cubes.

Paper Details

Date Published: 27 March 2001
PDF: 11 pages
Proc. SPIE 4384, Data Mining and Knowledge Discovery: Theory, Tools, and Technology III, (27 March 2001); doi: 10.1117/12.421092
Show Author Affiliations
Irwin E. Alber, Boeing Co. (United States)
Morton S. Farber, Boeing Co. (United States)
Nancy Yeager, Univ. of Illinois/Urbana-Champaign (United States)
Ziyou Xiong, Univ. of Illinois/Urbana-Champaign (United States)
William M. Pottenger, Univ. of Illinois/Urbana-Champaign and Lehigh Univ. (United States)


Published in SPIE Proceedings Vol. 4384:
Data Mining and Knowledge Discovery: Theory, Tools, and Technology III
Belur V. Dasarathy, Editor(s)

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