paper available: Local Feature Analysis

[Penio Penev]

The following paper just appeared:
Network: Computation in Neural Systems 7(3), 477-500, 1996

                     Local Feature Analysis: 
     A General Statistical Theory for Object Representation.

                Penio S. Penev and Joseph J. Atick

Low-dimensional representations of sensory signals are key to solving
many of the computational problems encountered in high-level vision.
Principal Component Analysis has been used in the past to derive
practically useful compact representations for different classes of
objects. One major objection to the applicability of PCA is that it
invariably leads to global, nontopographic representations that are
not amenable to further processing and are not biologically plausible.
In this paper we present a new mathematical construction---Local
Feature Analysis (LFA)---for deriving local topographic
representations for any class of objects.  The LFA representations are
sparse-distributed and, hence, are effectively low-dimensional and
retain all the advantages of the compact representations of the
PCA. But unlike the global eigenmodes, they give a description of
objects in terms of statistically derived local features and their
positions.  We illustrate the theory by using it to extract local
features for three ensembles---2D images of faces without background,
3D surfaces of human heads, and finally 2D faces on a background. The
resulting local representations have powerful applications in head
segmentation and face recognition.


For those having on-line access to the electronic version of the journal,
the paper can be retrieved from http://www.iop.org/EJ/welcome

There is also a version with LaTex fonts and USA spelling at our web and
ftp site venezia.rockefeller.edu.

The file is best viewed on a 600 dpi printer because it contains grayscale
images. 

FTP-host: venezia.rockefeller.edu
FTP-filename: group/papers/full/LFA/PenevPS.LFA.ps      -- 600 dpi
FTP-filename: group/papers/full/LFA/PenevPS.LFA.300.ps  -- 300 dpi

ftp://venezia.rockefeller.edu/group/papers/full/LFA/PenevPS.LFA.ps
ftp://venezia.rockefeller.edu/group/papers/full/LFA/PenevPS.LFA.300.ps

--
Penio Penev <Penev at pisa.Rockefeller.edu> 1-212-327-7423