Affine invariant image segmentation

This paper introduces a method of image segmentation that partitions an image into regions exhibiting self-similarity. An affine texture de-scription which models an image as a collection of self-similar image blocks is developed. Transformations between prototype and target blocks are estimated by Gaussian Mixture Modelling of block spectra and least-squares estimation. An appropriate set of prototype blocks (codebook) is determined for a given an image by clustering in a fea-ture subspace of discrete samples from an affine symmetry group. The new method is well suited to the description and segmentation of images containing textures, such as fingerprints and medical images and could be used as a basis for indexing and searching in CBIR applications. Ex-perimental results are presented which demonstrate the potential of the method on widely varying images.