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

  • Authors:

  • Etienne Baudrier;Gilles Millon;Frederic Nicolier;Su Ruan

  • Affiliations:

  • Laboratoire CReSTIC, IUT de Troyes, 9, rue de Québec, 10026 TROYES CEDEX, France;Laboratoire CReSTIC, IUT de Troyes, 9, rue de Québec, 10026 TROYES CEDEX, France;Laboratoire CReSTIC, IUT de Troyes, 9, rue de Québec, 10026 TROYES CEDEX, France;Laboratoire CReSTIC, IUT de Troyes, 9, rue de Québec, 10026 TROYES CEDEX, France

  • Venue:
  • ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
  • Year:
  • 2006

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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 usely 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 improve the dissimilarity information. Combined with a Support Vector Machine classifier, this method is successfully tested on an medieval-impression database.