Multiple Eigenspaces
Ales Leonardis
(joint work with H. Bischof, Vienna University of Technology,
and J. Maver, University of Ljubljana) .
U. of Lujbljana, Slovenia
In the talk I will describe a novel approach to constructing multiple,
low-dimensional eigenspaces from a set of training images. Grouping
of images is systematically and robustly performed via
eigenspace-growing in terms of low-dimensional eigenspaces.
To further increase the robustness, the eigenspace-growing is initiated
independently with many small (seed) groups of images. All these
grown eigenspaces are treated as hypotheses that are subject to a
selection procedure eigenspace-selection, based on the MDL
principle, which selects the final resulting set of eigenspaces as an optimal
representation of the training set, taking into account the number of
images encompassed by the eigenspaces, the dimension of the eigenspaces, and
their corresponding residual errors.
We have tested the proposed method
on a number of standard image sets, and the significance of the
approach with respect to the recognition rate has been clearly demonstrated.