BMVC 2004, Kingston, 7th-9th Sept, 2004

Image normalization by mutual information
E. Bart and S. Ullman (Weizmann Institute of Science, Israel)

Image normalization refers to eliminating image variations (such as noise,
illumination, or occlusion) that are related to conditions of image acquisition
and are irrelevant to object identity. Image normalization can be used as a
preprocessing stage to assist computer or human object perception. In this
paper, a class-based image normalization method is proposed. Objects in
this method are represented in the PCA basis, and mutual information is used
to identify irrelevant principal components. These components are then discarded
to obtain a normalized image which is not affected by the specific
conditions of image acquisition. The method is demonstrated to produce visually
pleasing results and to improve significantly the accuracy of known
recognition algorithms.
The use of mutual information is a significant advantage over the standard
method of discarding components according to the eigenvalues, since eigenvalues
correspond to variance and have no direct relation to the relevance
of components to representation. An additional advantage of the proposed
algorithm is that many types of image variations are handled in a unified
(pdf article)