We propose using different classifiers based on the spatial location of
the object. The intuitive idea behind this approach is that several
classifiers may learn local concepts better than a "universal" classifier
that covers the whole feature space. The use of local classifiers ensures
that the objects of a particular class have a higher degree of
resemblance within that particular class. The use of local classifiers
also results in memory, storage and performance improvements, especially
when the classifier is kernel-based. As used herein, the term
"kernel-based classifier" refers to a classifier where a mapping function
(i.e., the kernel) has been used to map the original training data to a
higher dimensional space where the classification task may be easier.