Subimage extraction by integer-type lifting wavelet transforms

This paper proposes a method for extracting subimages from a huge reference image by using lifting wavelet transforms that map integers to integers. Our integer-type lifting wavelet transform contains controllable free parameters, which are constructed based on an integer version of the Haar transform. Our learning method is to determine such free parameters using some subimages so as to remove their high frequency components in the y- and x-directions. The learnt wavelet transform has the feature of the subimages. We apply such a wavelet transform to high frequency components of a reference image and check whether they are removed or not, to detect a target subimage.