A new similarity measure for histograms applied to content-based retrieval of medical images
Proceedings of the 2006 ACM symposium on Applied computing
Easing the Dimensionality Curse by Stretching Metric Spaces
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
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This work presents a new distance functionthe Global Warp Metric Distance - to compare histograms used as a feature to index image databases in content-based image retrieval environments. The Metric Histogram represents a compact, but efficient alternative to the use of traditional gray-level histograms to represent images. The Global Warp Metric Distance (GWMD) enhances the comparison between histograms, replacing the rigid bin-to-bin evaluation by the Warp method, which allows a local "adjustment" of one histogram to the other during the distance calculation, introducing a global matching of the curves. Besides this, GWMD applies a set of geometric global features of histograms to determine the final distance. Results on similarity retrieval in medical images demonstrate the superiority of the proposed approach in analyzing image sets that present brightness and contrast disparities: it reduces the amount of both false positive and false negative retrievals. Moreover, these results comply with similarity evaluations performed by domain specialists.