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Proceedings Paper

Similarity pyramids for browsing and organization of large image databases
Author(s): Jau-Yuen Chen; Charles A. Bouman; John C. Dalton
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Paper Abstract

The advent of large image databases (> 10,000) has created a need for tools which can search and organize image automatically by their content. This paper presents a method for designing a hierarchical browsing environment which we call a similarity pyramid. The similarity pyramid groups similar images together while allowing users to view the database at varying levels of resolution. We show that the similarity pyramid is best constructed using agglomerative (bottom-up) clustering methods, and present a fast-sparse clustering method which dramatically reduces both memory and computation over conventional methods. We then present an objective measure of pyramid organization called dispersion, and we use it to show that our fast-sparse clustering method produces better similarity pyramids than top down approaches.

Paper Details

Date Published: 17 July 1998
PDF: 13 pages
Proc. SPIE 3299, Human Vision and Electronic Imaging III, (17 July 1998); doi: 10.1117/12.320147
Show Author Affiliations
Jau-Yuen Chen, Purdue Univ. (United States)
Charles A. Bouman, Purdue Univ. (United States)
John C. Dalton, Ricoh California Research Ctr. (United States)

Published in SPIE Proceedings Vol. 3299:
Human Vision and Electronic Imaging III
Bernice E. Rogowitz; Thrasyvoulos N. Pappas, Editor(s)

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