Structural Pattern Recognition

It is a form of pattern recognition, in which each object can be represented by a variable-cardinality set of symbolic, nominal features. This allows for representing pattern structures, taking into account more complex interrelationships between attributes than is possible in the case of flat, numerical feature vectors of fixed dimensionality, that are used in statistical classification.One way to present such structure is by means of a strings of symbols from a formal language. In this case the differences in the structures of the classes are encoded as different grammars. A second way to represent relations are graphs, where nodes are connected if corresponding subpatterns are related. Structural methods provide description of items, which may useful on its own right. For example, syntactic pattern recognition can be used to find out what object are present in an image. Furthermore, structural methods are strong in finding a correspondence mapping between two images of an object. Under natural conditions, corresponding features will be in different positions and/or may be occluded in the two images, due to camera-attitude and perspective, as in face recognition. A graph-matching algorithm will yield the optimal correspondence.


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Structural Pattern Recognition,It is a form of pattern recognition, in which each object can be represented by a variable-cardinality set of symbolic, nominal features. This allows for representing pattern structures, taking into account more complex interrelationships between attributes than is possible in the case of flat, numerical feature vectors of fixed dimensionality, that are used in statistical classification.One way to present such structure is by means of a strings of symbols from a formal language. In this case the differences in the structures of the classes are encoded as different grammars. A second way to represent relations are graphs, where nodes are connected if corresponding subpatterns are related. Structural methods provide description of items, which may useful on its own right. For example, syntactic pattern recognition can be used to find out what object are present in an image. Furthermore, structural methods are strong in finding a correspondence mapping between two images of an object. Under natural conditions, corresponding features will be in different positions and/or may be occluded in the two images, due to camera-attitude and perspective, as in face recognition. A graph-matching algorithm will yield the optimal correspondence.,