clustering & matching papers

[Eric Mjolsness]

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****** PAPER AVAILABLE VIA NEUROPROSE ***************************************

FTP-host: archive.cis.ohio-state.edu
FTP-filename: /pub/neuroprose/gold.object-clustering.ps.Z
FTP-filename: /pub/neuroprose/lu.object-matching.ps.Z

The following two NIPS papers have been placed in the Neuroprose archive
at Ohio State. The files are "gold.object-clustering.ps.Z" and
"lu.object-matching.ps.Z".  Each is  8 pages in length.  The uncompressed
postscript file for the second paper, "lu.object-matching.ps.Z", contains
images and is 4.3 megabytes long.  So you may need to use a symbolic link
in printing it: "lpr -s" under SunOS.


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Clustering with a Domain-Specific Distance Measure

Stephen Gold, Eric Mjolsness and Anand Rangarajan
Yale Computer Science Department

With a point matching distance measure which is invariant under
translation, rotation and permutation, we learn 2-D point-set objects,
by clustering noisy point-set images.  Unlike traditional clustering
methods which use distance measures that operate on feature vectors - a
representation common to most problem domains - this object-based
clustering technique employs a distance measure specific to a type of
object within a problem domain.  Formulating the clustering problem as
two nested objective functions, we derive optimization dynamics similar
to the Expectation-Maximization algorithm used in mixture models.

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Two-Dimensional Object Localization by Coarse-to-Fine Correlation
Matching

Chien-Ping Lu and Eric Mjolsness
Yale Computer Science Department

We present a Mean Field Theory method for locating two-dimensional
objects that have undergone rigid transformations.  The resulting
algorithm is a coarse-to-fine correlation matching.  We first consider
problems of matching synthetic point data, and derive a point matching
objective function.  A tractable line segment matching objective
function is derived by considering each line segment as a dense
collection of points, and approximating it by a sum of Gaussians.  The
algorithm is tested on real images from which line segments are
extracted and matched.

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		- Eric Mjolsness
		  mjolsness at cs.yale.edu


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