A new similarity measure using hausdorff distance map
Étienne Baudrier, Graeme Walter Milton, Frédéric Nicolier, Su Ruan · 2005
Image dissimilarity measure is a hot topic. The measuring process is generally composed of an information mining in each image which results in an image signature and then a signature comparison to make the decision about the image similarity. In the scope of binary images, we propose to replace the information mining by a new straight image comparison which does not require a priori knowledge. The second stage is then replaced by a decision process based on the image comparison. The new comparison process is structured as follows: a morphological multiresolution analysis is applied to the two images. Secondly a distance map is constructed at each scale by the computation of the Hausdorff distance, restricted through a sliding-window. A signature is then extracted from the distance map and is used to make the decision. As an application, the algorithm has been successfully tested on an ancient illustration database.