Classification in Noisy Environments Using a Distance Measure Between Structural Symbolic Descriptions
Issue No. 03 - March (1992 vol. 14)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/34.120333
<p>A definition of distance measure between structural descriptions that is based on a probabilistic interpretation of the matching predicate is proposed. It aims at coping with the problem of classification when noise causes both local and structural deformations. The distance measure is defined according to a top-down evaluation scheme: distance between disjunctions of conjuncts, conjunctions, and literals. At the lowest level, the similarity between a feature value in the pattern model (G) and the corresponding value in the observation (Ex) is defined as the probability of observing a greater distortion. The classification problem is approached by means of a multilayered framework in which the cases of single perfect match, no perfect match, and multiple perfect match are treated differently. A plausible solution for the problem of completing the attribute and structure spaces, based on the probabilistic approach, is also given. A comparison with other related works and an application in the domain of layout-based document recognition are presented.</p>
attribute spaces; pattern recognition; learning systems; noisy environments; distance measure; structural symbolic descriptions; probabilistic interpretation; matching predicate; top-down evaluation; feature value; pattern model; classification; structure spaces; layout-based document recognition; learning systems; pattern recognition; probability
F. Esposito, G. Semeraro and D. Malerba, "Classification in Noisy Environments Using a Distance Measure Between Structural Symbolic Descriptions," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 14, no. , pp. 390-402, 1992.