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Summary: Feature Normalization and Likelihood-based
Similarity Measures for Image Retrieval
Selim Aksoy and Robert M. Haralick
Intelligent Systems Laboratory, Department of Electrical Engineering,
University of Washington, Seattle, WA 98195-2500, U.S.A.
E-mail:{aksoy,haralick}@isl.ee.washington.edu
Abstract
Distance measures like the Euclidean distance are used to measure similarity be-
tween images in content-based image retrieval. Such geometric measures implicitly
assign more weighting to features with large ranges than those with small ranges.
This paper discusses the effects of five feature normalization methods on retrieval
performance. We also describe two likelihood ratio-based similarity measures that
perform significantly better than the commonly used geometric approaches like the
Lp metrics.
Key words: Feature normalization; Minkowsky metric; likelihood ratio; image
retrieval; image similarity
1 Introduction
Image database retrieval has become a very popular research area in recent
years (Rui et al. (1999)). Initial work on content-based retrieval (Flickner et al.
(1993); Pentland et al. (1994); Manjunath and Ma (1996)) focused on using
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