Template Learning from Atomic Representations: A Wavelet-based Approach
to Pattern Analysis
Abstract
Despite the success of wavelet decompositions in other areas of
statistical signal and image processing, current wavelet-based image
models are inadequate for modeling patterns in images, due to the presence
of unknown transformations (e.g., translation, rotation, location of
lighting source) inherent in most pattern observations. In this paper we
introduce a hierarchical wavelet-based framework for modeling patterns in
digital images. This framework takes advantage of the efficient image
representations afforded by wavelets, while accounting for unknown pattern
transformations. Given a trained model, we can use this framework to
synthesize pattern observations. If the model parameters are unknown, we
can infer them from labeled training data using TEMPLAR (Template Learning
from Atomic Representations), a novel template learning algorithm with
linear complexity. TEMPLAR employs minimum description length (MDL)
complexity regularization to learn a template with a sparse representation
in the wavelet domain. We discuss several applications, including
template learning, pattern classification, and image registration.
postscript
pdf
(25 pages)