preprint available

[zemel@cs.toronto.edu]

The following paper has been placed in the neuroprose archives
at Ohio State University:


	Discovering Viewpoint-Invariant Relationships
	       That Characterize Objects


           Richard S. Zemel & Geoffrey E. Hinton
	      Department of Computer Science
 		University of Toronto
	     Toronto, Ont. CANADA  M5S-1A4


 Abstract

Using an unsupervised learning procedure, a network is trained on an ensemble
of images of the same two-dimensional object at different positions,
orientations and sizes.  Each half of the network ``sees'' one fragment of the
object, and tries to produce as output a set of 4 parameters that have high
mutual information with the 4 parameters output by the other half of the
network.  Given the ensemble of training patterns, the 4 parameters on which
the two halves of the network can agree are the position, orientation, and
size of the whole object, or some recoding of them.  After training, the
network can reject instances of other shapes by using the fact that the
predictions made by its two halves disagree.  If two competing networks are
trained on an unlabelled mixture of images of two objects, they cluster the
training cases on the basis of the objects' shapes, independently of the
position, orientation, and size.




This paper will appear in the NIPS-90 proceedings.


To retrieve it by anonymous ftp, do the following:

unix> ftp cheops.cis.ohio-state.edu          # (or ftp 128.146.8.62)
Name (cheops.cis.ohio-state.edu:): anonymous
Password (cheops.cis.ohio-state.edu:anonymous): <ret>
ftp> cd pub/neuroprose
ftp> binary
ftp> get zemel.unsup-recog.ps.Z
ftp> quit
unix>
unix> zcat zemel.unsup-recog.ps.Z | lpr -P<your postscript printer>