Multiscale Branch and Bound Image Database Search (1997)
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| Venue: | In SPIE: Storage and Retrieval for Image and Video Databases V |
| Citations: | 12 - 2 self |
BibTeX
@INPROCEEDINGS{Chen97multiscalebranch,
author = {Jau-yuen Chen and Charles A. Bouman and Jan P. Allebach},
title = {Multiscale Branch and Bound Image Database Search},
booktitle = {In SPIE: Storage and Retrieval for Image and Video Databases V},
year = {1997},
pages = {133--144}
}
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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 define 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 L 1 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. Keyword: multiscale search, image similarity, content-based retrieval, color histogram, convex function 1 INTRODUCTION One method for searching large databases of natural images is to retrieve images with color...
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