Global Analysis as Clustering Process.
Ignoring the spatial coordinates of the feature data, we are left with
a set of feature values f approximating a global probability distribution
P(f). Analysis of P(f) can be done via a variety
of cluster algorithms. These algorithms search prominent clusters in the
data set and compute some prototypical representation 
for each cluster. In a second processing step, all data is grouped into
one of the found clusters by associating each f(x) to the
nearest prototype
.
Since in image analysis tasks the available data base for building the
prototypical feature vectors
is large, the central limit theorem assures stable performance of these
types of algorithms. The intrinsic properties of the physical world -- which
created the data in first place -- assure normally connectedness of
the detected segments in input space (despite the fact that the spatial
information was not used in the grouping operation). However, strong noise
will cause algorithms based on global analysis to fail (figure 1.b).
If the distributions of feature values corresponding to different objects
overlap, detected regions will have many holes or they may even consist
of several disconnected patches. In addition, regions far apart from each
other, but possessing approximately equal feature values, will be grouped
into a single object. Both types of errors can be attributed to the neglect
of spatial information in a global analysis.