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 eects of v e feature normalization methods on retrieval performance. We also describe two likelihood ratio-based similarity measures that perform signican tly better than the commonly used geometric approaches like the Lp metrics.
['Selim Aksoy', 'Robert M. Haralick']
Feature normalization and likelihood-based similarity measures for image retrieval