Abstract
A method for learning shapes structurally described by means of attributed relational graphs (ARG's) is discussed and tested. The method is based on an algorithm that, starting from a set of labeled shapes, finds out the set of maximally general prototypes. These prototypes, given in terms of a suitably defined data structure which generalizes the ARG's, satisfy the properties of completeness and consistency with reference to the training set, and result to be particularly effective for their interpretability. After resuming the algorithm, the paper addresses the problem of shape representation by ARG's, and then presents the experimental results of a learning task, with reference to a database of artificial images generated by a set of attributed plex grammars. The main focus here is not on the learning algorithm, but on its applicability to the problem of learning shapes from examples. A discussion of the results, aimed to highlight pros and cons, is finally reported.
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