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 by hierarchical system of patterns which can take into account more complex interrelationships between features than simple numerical [[feature vector]]s used in [[statistical classification]].

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'''Syntactic pattern recognition''', or '''structural pattern recognition''', is a form of [[pattern recognition]], wherein itemswhich areeach object can be presentedrepresented by hierarchicala systemvariable-[[cardinality]] set of patternssymbolic which[[nominal candata|nominal]] [[Feature (machine learning)|features]]. This allows for representing pattern structures, taketaking into account more complex interrelationshipsrelationships between featuresattributes than simpleis 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 presentationstructure is via [[String (computer science)|strings]] of symbols from a [[formal language]]. In this case, itemsthe aredifferences classifiedin tothe certainstructures classof ifthe theyclasses canare beencoded [[parsing|parsed]]as by correspondingdifferent [[formal grammar|grammargrammars]].

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.

Structural methods provide description of items, which may useful on its own right.

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.

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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]]