The present work describes a new approach to integrating structural
knowledge into the image classification process. First, a fuzzy
classifier produces a fuzzy partition of the image. Then, the
defuzzified (crisp) partition is tried to be improved. According to the
membership degrees in the fuzzy partition, the system selects a set of
pixels and associates a set of candidate classes with each of them. The
initial crisp partition is improved by reassigning each selected pixel
to one of the classes it may belong to. This is performed by a
combinatorial optimization strategy. The aim is to maximize the
adequacy between the regions defined by the crisp partition and the
structural knowledge which is available. First experiments on synthetic
data as well as on simple real data show the applicability of our
approach.
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