Slow feature analysis and decorrelation filtering for separating correlated images

Applied and Computational Mathematics Seminar
Wednesday, June 13, 2012 - 11:00
1 hour (actually 50 minutes)
Skiles 255
Italian Institute of Technology
Slow Feature Analysis (SFA) is a method for extracting slowly varying features from input signals.  In this talk, we  generalize SFA  to vector-valued functions of multivariables and apply it to the problem of blind source separation, in particular image separation. When the sources are correlated, we apply the following technique called decorrelation filtering: use a linear filter to decorrelate the sources and their derivatives, then apply the separating matrix obtained on the filtered sources to the original sources. We show that if the filtered sources are  perfectly separated by this matrix, then so are the original sources.We show how to numerically obtain such a decorrelation filter by solving a nonlinear optimization problem. This technique can also be applied to other  linear separation methods, whose output signals are uncorrelated, such as  ICA.This is joint work with Laurenz Wiskott (Proceedings of the 13th IEEE International Conference in Computer Vision, ICCV 2011, Barcelona, Spain).