No subject
Reiner Lenz
reiner at isy.liu.se
Mon Apr 3 03:59:33 EDT 1995
jaaskelainen at joyl.joensuu.fi
Subject: Paper on Neuroprose: lenz.colorpca.ps.Z, Unsupervised Filtering of Color Spectra
FTP-host: archive.cis.ohio-state.edu
FTP-file: pub/neuroprose/lenz.colorpac.ps.Z
HTTP: ftp://archive.cis.ohio-state.edu/pub/neuroprose/lenz.colorpac.ps.Z
The following paper has been placed in the Neuroprose archive at
Ohio State University:
Title: Unsupervised Filtering of Color Spectra
Reiner Lenz, Mats \"Osterberg, Dept. EE, Link\"opin g University,
S-58183 Link\"oping, Sweden,
Jouni Hiltunen, Timo Jaaskelainen, Dept. Physics, University of
Joensuu, FIN-80101 Joensuu,Finland
Jussi Parkkinen, Dept. Information Technology, Lappeenranta
University of Technology, FIN-53851 Lappeenranta, Finland
Abstract
We describe how unsupervised neural networks can be used to extract
features from databases of reflectance spectra. These databases try to
sample color space in a way which reflects the properties of human
color perception.
In our construction of neural networks we identify first desirable
properties of the network. These properties are then incorporated into
an energy function and finally a learning rule is derived using
optimization methods to find weight matrices which lead to minimal
values of the energy function. We describe several energy
functions and the performance of the resulting networks for the
databases with the reflectance spectra. The experiments show that the
weight matrix for one of the systems is very similar to the
eigenvector system whereas the second type of systems tries to rotate
the eigenvector system in such a way that the resulting filters
partition the spectrum into different bands. We will also show how the
additional constraint of positive filter coefficients can be
incorporated into the design.
It will appear in the Proc. Scandinavian Conference Image Analysis,
Uppsala, 1995.
(8 pages. No hard copies available.)
_______________________________
More information about the unsupervised network used in the paper can be found
in the PhD thesis:
M. O"sterber: Quality functions for parallel selective principal component
analysis. ISBN 91-7871-411-7
_______________________________
"Kleinphi macht auch Mist"
Reiner Lenz | Dept. EE. |
| Linkoeping University | email: reiner at isy.liu.se
| S-58183 Linkoeping/Sweden |
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