Matching pursuit of images

A crucial problem in image analysis is to construct efficient low-level representations of an image, providing informations on features such as edges and texture components. An image usually contains very different types of features, which have been successfully modelized by the very redundant family of 2D Gabor functions. However, this model generates representations with a very large size. Instead of decomposing a given image over this whole set of Gabor functions, we use an adaptative algorithm to select a subset of Gabor elements that are best adapted to the image. This produces compact representation in terms of few features that reveal the local image properties