Syntactic pattern recognition: Difference between revisions

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{{Short description|Form of pattern recognition}}

'''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 vector]]s used in [[statistical classification]].

{{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 there is 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 diagnosis of thediagnosing [[heart]] problems with [[Electrocardiogram|ECGelectrocardiogram]] (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, measured ECG signalsignals can be classified as healthy or unhealthy by first describing itthem in termterms of the basic line segments, and then trying to parse the descriptions according to the grammars. Another example is [[tessellation]] otof Tilingtiling patterns.

AnotherA second way to represent relations are [[Graph (discrete mathematics)|graphs]], where nodes are connectedlinked if corresponding subpatterns are related. An item can be labeledassigned asa belongingcertain toclass a classlabel if its graph representation is [[isomorphic]] with prototype graphs of thethat class.

Typically, patterns are constructed from simpler subpatternssub-patterns in a hierarchical fashion. This helps in dividingdivide the recognition task into easier subtasksubtasks of first identifying subpatternssub-patterns, and only then the actual patterns.

Structural methods provide descriptiondescriptions of items, which may be useful onin itstheir own right. For example, syntactic pattern recognition can be used to find outdetermine what object[[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 = Schalkoff Flasinski| first = RobertMariusz | title = PatternSyntactic pattern recognition - statistical, structural and neural approaches | publisher = JohnWorld Wiley & sonsScientific | year = 19922019 | idISBN = ISBN 0978-981-4713278-5523846-02 }}

[[Category:Classification algorithms]]