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.

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.