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that it can always map a new projected image point on to the leave surfaces of
the structure. Here is the significance of SOM over the back-propagation
learning. For a new input-output pattern the back-propagation net is to be retrained,
whereas for a new input pattern the incremental classification of SOM
just maps the new point onto one of the existing leaves of the 2-D surfaces.
After the structural organization of the SOM is over, we can classify a
test image by the structure. Let IP be the projected point corresponding to the
test image. Let W be the weight vector of a neuron. We first compute the ||IP
–W || for all neurons mapped on the surface at level 0 and identify the neuron
where the measure is minimum. We then compute ||IP –W || for all neurons
of a surface in level 1, which is pointed to by the winning neuron at level 0.
The process is thus continued until one of the leaves of the structure is
reached. The search procedure in a SOM thus is analogous to best first search.
17.4.1.2 Non-Neural Approaches
for Image Recognition
There exist quite a large number of non-neural approaches for pattern
recognition. For instance Bayes’ classifier, rule based classifier, and fuzzy
classifier are some of the well-known techniques for pattern classification,
which have also applications in recognition of human faces. The simplest
among them is the rule based classification. The range of parameters of the
feature space is mentioned in the premise part of the rules. When all the
preconditions are jointly satisfied, the rule fires classifying the object. One
illustrative rule for classification of sky is presented below.
Rule: If (the location-of-the-region = upper) and
( Intensity-of-the-region-is-in {0.4,0.7}) and
(Color-of-region = (blue or grey)) and
(Texture-of-the-region-is-in {0.8 , 1})
Then (region = sky).
One drawback of the rule based system is that in many occasions the
precondition of no rules are supported by the available data space. Fuzzification
of the data space is required under this circumstance to identify the rules having
partially matched preconditions. The application of such fuzzy rule based
systems for facial image matching will be presented in a subsequent chapter of
this book. Bayes’ classification rule and fuzzy c-means classification schemes
are beyond the scope of this book.