Recognition vs. Similarity

Contents
1 » The nature of image recognition3 » Capability Comparison
2 » The nature of image similarity
The nature of image recognition

Image recognition is an internal process operating on an object in a scene:

  • Observer can determine an object using only prior knowledge of what to look for.
  • Objects recognised according to the discovery of expected characteristics.
  • Algorithms designed to match visual data against expectations.
  • Often requires a different algorithm for each class of object to be recognised.
  • If there is insufficient prior knowledge then recognition fails.
  • If the context is unknown or the image is ambiguous then recognition fails.

Recognition is most suited to situations where objects within a scene need to be identified independently of the overall content of the scene. It is usually performed by searching the image for characteristic elements of known objects, such as shape or colour. Algorithms for specific applications, such as finding people or car registration numbers, are common, effective, and robust.

Since almost any image analysis task can be expressed by algorithms working on prior knowledge of what to look for in an image, the term ‘image recognition’ can span almost any analysis function. By extension, the same principle can be applied to other forms of data such as audio or other arbitrary data-sets; for example, to identify a specific rendition of a musical score.

ProsCons
Good accuracy if algorithm is robust.Requires multiple object-specific algorithms.
Can be made to suit almost any application.Speed varies according to each algorithm.
No database.Requires high computational power.
Low resource requirements.Only as good as the prior knowledge.
Can fail if image does not meet algorithm criteria.
Not suited to arbitrary whole-scene comparisons.
The nature of image similarity

Image similarity is an external process operating over a whole scene:

  • Observer compares two scenes against each other.
  • Calculates the probability that two scenes are similar.
  • Does not require any prior knowledge.
  • Uses a database of sample images of each class of scenes.
  • A single algorithm works with all classes of scenes according to database.
  • A scene containing a single object gives similar functionality to image recognition.

Similarity is most suited to situations where an imprecise determination is required of what a scene depicts, e.g. ‘Is this unknown painting most likely a Van Gogh or a Monet?’. It is usually performed by generating a suitable signature of an image, where the difference between the signatures of two images is a measure of the distance between the two images (a distance of 0 means the images are identical; a distance of 1 means they are completely different). It is not so suited to finding single objects within a scene unless the scene depicts only a single object.

Since the calculation of similarity is performed on a signature, not on the visual image, then in principle the signature can be derived from any data source, allowing the single algorithm to operate not only on images, but also on audio or other arbitrary data sets; for example, to compare the similarity of musical performances, or even to synaesthetically measure the similarity of an image to a piece of music.

ProsCons
Needs only a single algorithm for comparison of signatures.No single interpretation of what constitutes similarity.
Requires no prior knowledge of objects.Requires context-dependent judgements of what is important.
New objects can be found by adding samples to a database.May need multiple feature-extraction algorithms.
Database samples can be of disparate nature, e.g. photos, sketches, clip-art.Quality of result depends on the quality of the database.
Is tolerant of variations between images.Not directly suited to identifying objects within a scene.
Can be applied to an entire arbitrary scene.Has moderate resource requirements.
Has low computational requirements.
Speeds of millions of comparisons per second, independent of scene complexity.
Capability Comparison
MeasureRecognitionSimilarity
Conceptual natureAnalyticalIntuitive
ApplicabilitySpecific inspectionGeneral impression
Basis of capabilitiesPrior knowledgeDatabase of samples
ComplexityHighLow
Number of algorithmsManyOne
SpeedLowHigh
AccuracyHigh; ObjectiveGood; Subjective
Resources usedLow ~ MediumMedium ~ High
Computing powerMedium ~ HighLow

Technology in brief

An image processing technology capable of, amongst other things, measuring the similarity of images, recognising contents, and understanding scenes.

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Recognition vs. Similarity

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