Hausdorff distance-based multiresolution maps applied to image similarity measure

Image comparison is widely used in image processing. For binary images that are not composed of a single shape, a local comparison can be interesting because the features are usually poor (colour) or difficult to extract (texture, forms). Thus a new binary image comparison method that uses a windowed Hausdorff distance is presented. It enables local dissimilarities to be quantified in a simple way. The comparison results in a dissimilarity map. These maps are then used to evaluate the image similarity. The evaluation uses a classification step that is based on a comparison of the dissimilarity map histogram with reference histograms. The comparison is carried out at different scales of a multiresolution analysis, allowing the most discriminating scale in a user-defined notion of dissimilarity to be chosen automatically in the learning step. As an application, a database of digitalized ancient illustrations is successfully processed by the new method.