Syntactic pattern recognition
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Syntactic pattern recognition or structural pattern recognition is a form of pattern recognition, where items are presented pattern structures which can take into account more complex interrelationships between features than simple numerical feature vectors used in statistical classification.
Syntactic pattern recognition can be used (instead of statistical pattern recognition) if there is clear structure in the patterns. One way to present such structure is strings of a formal language. In this case differences in the structures of the classes are encoded as different grammars.
An example of this would be diagnosis of the heart with ECG measurements. ECG waveforms can be approximated with diagonal and vertical line segments. If normal and unhealthy waveforms can be described as formal grammars, measured ECG signal can be classified as healthy or unhealthy by first describing it in term of the basic line segments and then trying to parse the descriptions according to the grammars.
Another way to represent relations are graphs, where nodes are connected if corresponding subpatterns are related. An item can be labeled as belonging to a class if its graph representation is isomorphic with prototype graphs of the class.
Typically, patterns are constructed from simpler subpatterns in a hierarchical fashion. This helps in dividing the recognition task into easier subtask of first identifying subpatterns and only then the actual patterns.
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.
[edit] References
Schalkoff, Robert (1992). Pattern recognition - statistical, structural and neural approaches. John Wiley & sons. ISBN 0-471-55238-0.
