Multiscale branch-and-bound image database search

Jau-Yuen Chen; Charles A. Bouman; Jan P. Allebach

doi:10.1117/12.263402

15 January 1997 Multiscale branch-and-bound image database search

Jau-Yuen Chen, Charles A. Bouman, Jan P. Allebach

Proceedings Volume 3022, Storage and Retrieval for Image and Video Databases V; (1997) https://doi.org/10.1117/12.263402
Event: Electronic Imaging '97, 1997, San Jose, CA, United States

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.

Citation Download Citation

Jau-Yuen Chen, Charles A. Bouman, and Jan P. Allebach "Multiscale branch-and-bound image database search", Proc. SPIE 3022, Storage and Retrieval for Image and Video Databases V, (15 January 1997); https://doi.org/10.1117/12.263402

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