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Proceedings Paper

Multiscale branch-and-bound image database search
Author(s): Jau-Yuen Chen; Charles A. Bouman; Jan P. Allebach
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Paper Abstract

This paper presents a formal framework for designing search algorithms which can identify target images by the spatial distribution of color, edge and texture attributes. The framework is based on a multiscale representation of both the image data, and the associated parameter space that must be searched. We defined a general form for the distance function which insures that branch and bound search can be used to find the globally optimal match. Our distance function depends on the choice of a convex measure of feature distance. For this purpose, we propose the L1 norm and some other alternative choices such as the Kullback-Liebler and divergence distances. Experimental results indicate that the multiscale approach can improve search performance with minimal computational cost.

Paper Details

Date Published: 15 January 1997
PDF: 12 pages
Proc. SPIE 3022, Storage and Retrieval for Image and Video Databases V, (15 January 1997); doi: 10.1117/12.263402
Show Author Affiliations
Jau-Yuen Chen, Purdue University (United States)
Charles A. Bouman, Purdue University (United States)
Jan P. Allebach, Purdue University (United States)


Published in SPIE Proceedings Vol. 3022:
Storage and Retrieval for Image and Video Databases V
Ishwar K. Sethi; Ramesh C. Jain, Editor(s)

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