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

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. Keywords Image comparison, binary images, Hausdorff distance, local dissimilarity measure, classification, SVM.

• Type: Conference
• Year: 2006
• Where: ICPR
• Authors: Etienne Baudrier, Frédéric Nicolier, Gilles Millon, Su Ruan
• Keywords: Binary Image Comparison, Computer Vision, ICPR 2006, Image Similarity Measure, Keywords Image Comparison