imported>Perceptron599: Intro improvements

2024-11-15T00:32:34Z

Intro improvements

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{{Short description|Form of pattern recognition}}
{{no footnotes|date=November 2024}}
'''Syntactic pattern recognition''', or '''structural pattern recognition''', is a form of [[pattern recognition]] in which each object can be represented by a variable-[[cardinality]] set of symbolic [[nominal data|nominal]] [[Feature (machine learning)|features]]. This allows for representing pattern structures, taking into account more complex relationships between attributes than is possible in the case of flat, numerical [[Feature (machine learning)#Feature vectors|feature vector]]s of fixed dimensionality that are used in [[statistical classification]].

Syntactic pattern recognition can be used instead of statistical pattern recognition if clear structure exists in the patterns. One way to present such structure is via [[String (computer science)|strings]] of symbols from a [[formal language]]. In this case, the differences in the structures of the classes are encoded as different [[formal grammar|grammars]].

An example of this would be diagnosing [[heart]] problems with [[electrocardiogram]] (ECG) measurements. ECG [[waveform]]s can be approximated with diagonal and vertical line segments. If normal and unhealthy waveforms can be described as formal grammars, ECG signals can be classified as healthy or unhealthy by first describing them in terms of the basic line segments, and then trying to parse the descriptions according to the grammars. Another example is [[tessellation]] of tiling patterns.

A second way to represent relations are [[Graph (discrete mathematics)|graphs]], where nodes are linked if corresponding subpatterns are related. An item can be assigned a certain class label if its graph representation is [[isomorphic]] with prototype graphs of that class.

Typically, patterns are constructed from simpler sub-patterns in a hierarchical fashion. This helps divide the recognition task into easier subtasks of first identifying sub-patterns, and then the actual patterns.

Structural methods provide descriptions of items, which may be useful in their own right. For example, syntactic pattern recognition can be used to determine what [[Object detection|objects are present in an image]]. Furthermore, structural methods are strong when applied to 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.

== See also==
* [[Grammar induction]]
* [[String matching]]
* [[Hopcroft–Karp algorithm]]
* [[Structural information theory]]

== References ==
{{cite book | last = Schalkoff | first = Robert | title = Pattern recognition - statistical, structural and neural approaches | publisher = John Wiley & sons | year = 1992 | isbn = 0-471-55238-0 }}

{{cite book | last= Bunke | first = Horst | title = Structural and syntactic pattern recognition, Chen, Pau & Wang (Eds.) Handbook of pattern recognition & computer vision | publisher = World Scientific | pages = 163–209 | year = 1993 | ISBN = 981-02-1136-8 }}

{{cite book | last= Flasinski| first = Mariusz | title = Syntactic pattern recognition | publisher = World Scientific | year = 2019 | ISBN = 978-981-3278-46-2 }}

[[Category:Classification algorithms]]

Syntactic pattern recognition - Revision history

imported>Perceptron599: Intro improvements