期刊
PATTERN RECOGNITION LETTERS
卷 22, 期 5, 页码 563-582出版社
ELSEVIER
DOI: 10.1016/S0167-8655(00)00112-4
关键词
feature normalization; Minkowsky metric; likelihood ratio; image retrieval; image similarity
Distance measures like the Euclidean distance are used to measure similarity between 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 L-p metrics. (C) 2001 Elsevier Science B.V. All rights reserved.
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