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1.2 Pattern Similarity and PR Tasks 5

colour
shape

For the green apple prototype we have therefore:

The points corresponding to the feature vectors of the prototypes are represented
by a square and a circle, respectively for the green apple and the orange, in Figure
1.4.
Let us consider a machine designed to separate green apples from oranges using
the described features. A piece of fruit is presented to the machine, its features are
computed and correspond to the point x (Figure 1.4a) in the colour-shape plane.
The machine, using the feature values as inputs, then has to decide if it is a green
apple or an orange. A reasonable decision is based on the Euclidian distance of the
point x from the prototypes, i.e., for the machine the similarity is a distance and in
this case it would decide "green apple". The output of the machine is in this case
any two-valued variable, e.g.. 0 corresponding to green apples and 1 corresponding
to oranges. Such a machine is called a classifier.

- -- .
1 40
green -,
green~ah ormge

oranges
red apple X~
7--4
O2;3O 040 050 060 070
a

Figure 1.4. (a) Green apples and oranges in the feature space; (b) A red apple
"resembling" an orange and a problematic greenish orange.

Imagine that our classifier receives as inputs the features of the red apple and
the greenish orange presented in Figure 1.2. The feature vectors correspond to the
points shown in Figure 1.4b. The red apple is wrongly classified as an orange since
it is much closer to the orange prototype than to the green apple prototype. This is
I not a surprise since, after all, the classifier is being used for an object clearly