computer science

Syntactic pattern recognition

Syntactic pattern recognition is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Syntactic pattern recognition rather than just read about it. In short: 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 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 vectors of fixed dimensionality that are used in…

Key takeaways

Syntactic pattern recognition belongs to computer science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Syntactic pattern recognition to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Syntactic pattern recognition from memory before moving on to harder problems.

Reference excerpt

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 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 vectors 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 strings of symbols from a formal language. In this case, the differences in the structures of the classes are encoded as different grammars. An example of this would be diagnosing heart problems with electrocardiogram (ECG) measurements. ECG waveforms 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 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 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 Schalkoff, Robert (1992). Pattern recognition - statistical, structural and neural approaches. John Wiley & sons. ISBN 0-471-55238-0. Bunke, Horst (1993). Structural and syntactic pattern recognition, Chen, Pau & Wang (Eds.) Handbook of pattern recognition & computer vision. World Scientific. pp. 163–209. ISBN 981-02-1136-8. Flasinski, Mariusz (2019). Syntactic pattern recognition. World Scientific. ISBN 978-981-3278-46-2.

Worked examples

Example 1 — a first encounter with Syntactic pattern recognition

Start with the simplest possible case. Write down what Syntactic pattern recognition claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Syntactic pattern recognition before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Syntactic pattern recognition ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Syntactic pattern recognition

In research: Syntactic pattern recognition appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Syntactic pattern recognition in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Syntactic pattern recognition is common in secondary-school and first-year university syllabi. It links to neighbouring topics Classification algorithms, so understanding it makes those chapters shorter.
In everyday life: Look for Syntactic pattern recognition outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Syntactic pattern recognition in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Syntactic pattern recognition means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Syntactic pattern recognition out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Syntactic pattern recognition in simple terms?

Why does Syntactic pattern recognition matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Syntactic pattern recognition?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Syntactic pattern recognition.