Syntactic pattern recognition: Difference between revisions

Revision as of 14:31, 31 January 2016

Latest revision as of 00:32, 15 November 2024

(3 intermediate revisions by 3 users not shown)

Line 1:

{{Short description|Form of pattern recognition}}

'''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]] 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 vector]]s of fixed dimensionality, that are 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 interrelationshipsrelationships 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 by means of avia [[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]] of tiling patterns.

A second way to represent relations are [[Graph (discrete mathematics)|graphs]]{{dn|date=January 2016}}, 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 sub -patterns in a hierarchical fashion. This helps in dividingdivide the recognition task into easier subtasksubtasks of first identifying sub -patterns, and only 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 find outdetermine what [[Object detection|objects are present in an image]]. Furthermore, structural methods are strong inwhen 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==

Line 21 ⟶ 23:

{{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]]