23, 1999, 1:00
Dr. Venu Govindaraju
State University of New York at Buffalo
Adaptive Pattern Recognition
methods for classifiers has been a topic of great interest to
the Pattern Recognition community. Several combination strategies
of serial and parallel combination have been developed to address
specific application needs. However, a general combination theory
has been lacking. We will present the research issues that must
be addressed in order to develop such a general theory in the
context of handwritten word recognizers. One of the issues is
the notion of adaptive word recognition. The idea is that a recognizer
must dynamically adapt to the classes (lexicon) it is discriminating.
It follows that our first need is for a character recognizer that
can adapt to its operating environment. Our presentation will
focus on adaptive character recognition. We will present a method
where features at different resolutions, from coarse to fine-grained,
are implemented by means of a recursive classification scheme.
Typically, recognizers have to balance the use of features at
many resolutions (which yields a high accuracy), with the burden
on computational resources in terms of storage space and processing
time. We present a method that adaptively determines the degree
of resolution necessary in order to classify an input pattern.
This leads to optimal use of computational resources. The adaptive
character recognizer dynamically adapts to factors such as the
quality of the input pattern, its intrinsic similarities and differences
from patterns of other classes it is being compared against and
the processing time available. Furthermore, the finer resolution
is accorded to only certain "zones'' of the input pattern
which are deemed important given the classes that are being discriminated.
Experimental results support the methodology presented. -------------
Biography: Venu Govindaraju received his PhD in computer science
from the State University of new York at Buffalo in 1992. He has
coauthored more than 80 technical papers in various International
journals and conferences and has one US patent. He is currently
the associate director of CEDAR and concurrently holds the research
associate professor ship in the department of Computer Science
and Engineering, State University of New York at Buffalo. He is
the associate editor of the Journal of Pattern Recognition and
the area chair of the IEEE SMC technical committee for pattern
recognition. Dr. Govindaraju has been a co-principal investigator
on several federally sponsored and industry sponsored projects.
He is presently leading multiple projects on postal applications.
He is a senior member of the IEEE.