A Discriminant Function for Noisy Pattern Recognition
Shin'ichiro Omachi, Fang Sun, and Hirotomo Aso
Proceedings of The 11th Scandinavian Conference on Image Analysis (SCIA'99),
pp.793-800, June 1999
For a practical pattern recognition system, noisy pattern recognition
is necessary and important.
There are several basic ideas of recognizing noisy patterns,
for example, constructing a dictionary with noisy patterns, applying different
classifiers, or using filters to delete noise.
In most conventional statistical pattern recognition methods,
a feature vector is extracted from an object.
The distribution of feature vectors is estimated for each category
to select a candidate for an unknown input pattern.
As it is known,
the distribution of feature vectors will change if noise occurs.
It is impossible to predict all kinds of noise that happen
accidentally and irregularly.
In this paper, an attempt to deal with noisy patterns is examined,
and a new discriminant function is proposed.
By introducing a revision matrix calculated from an unknown
the new function can be defined as a revised form of traditional
function with the degree of detected noise of each individual pattern.
The ability of the proposed function is evaluated by experiments
on discrimination of two categories.
Experimental results not only show the classification effectiveness of
the proposed function,
but also confirm that it is enormously important to detect the noise and
to revise the discriminant function for noisy pattern recognition.