A fast binary-image comparison method with local-dissimilarity quantification

Author

Baudrier, Etienne ; Millon, Gilles ; Nicolier, Frédéric ; Ruan, Su

Author_Institution

Lab. CReSTIC, Troyes

Volume

3

fYear

0

fDate

0-0 0

Firstpage

216

Lastpage

219

Abstract

Image similarity measure is widely used in image processing. For binary images that are not composed of a single shape, a local comparison is interesting but the features are usually poor (color) or difficult to extract (texture, forms). We present a new binary image comparison method that uses a windowed Hausdorff distance in a pixel-adaptive way. It enables to quantify the local dissimilarities and to give their spatial distribution which greatly improves the dissimilarity information. Combined with a support vector machine classifier, this method is successfully tested on a medieval-impression database

Keywords

computational geometry; image matching; support vector machines; Hausdorff distance; binary image comparison; dissimilarity information; feature extraction; image processing; image similarity measure; local dissimilarity quantification; medieval-impression database; spatial distribution; support vector machine classifier; Data mining; Feature extraction; High definition video; Image classification; Image processing; Image retrieval; Magnetohydrodynamics; Shape measurement; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 2006. ICPR 2006. 18th International Conference on

Conference_Location

Hong Kong

ISSN

1051-4651

Print_ISBN

0-7695-2521-0

Type

conf

DOI

10.1109/ICPR.2006.63

Filename

1699505