Web-based image search engine
Using a convenient AJAX-based interface, the similarity server provides a useful search engine with which a user selects a database, submits a query image, and the most similar images within the database are returned. Databases of millions of images are easily and quickly handled with searches through such databases being completed in as little as one second.
Of course, there is the question: “What constitutes similarity?”. The answer to this is not as simple as may be at first assumed. Is Snoopy more similar to a real beagle than he is to Garfield? It depends on whether the measure of similarity relates to what Snoopy is (a cartoon drawing) or what he represents (a beagle). As such, truly generic similarity is immeasurable by any single method. To cope with this dilemma, the similarity server uses a reconfigurable Idemetric Processor to allow different types of images to be measured for similarity in different ways.
Once configured for a particular type of image, all processing of queries is automatic without any user decisions or manipulations, and all required configurations can be stored and automatically applied on-the-fly by the processor according to the type, content, and context of the image presented to it, thus making the system very autonomous.
Idemetrics is a very flexible technology and has many uses, including:
|Taxonomy||Identifying the species of plants and animals; discerning between spiral, barred, and elliptical galaxies.|
|Sorting||Assiging images to categories based on their content, such as people, cars, animals, landscapes.|
|Distribution||Determining the distribution of objects within a scene, such as locating and following a crowd.|
|Density||Determining the density or quantity of objects within a scene, such as how busy a street is, or measuring bacterial cultures.|
|Geography||Matching landscapes; identifying changes in landscapes.|
|Cartography||Matching OS-style maps and road-maps with aerial photographs.|
|OCR||Number-plate recognition; road-sign reading; font recognition; reading handwriting; spell-checking; interpreting sign-language.|
|Stylistic neutrality||Comparing images of similar things in different styles, such as matching photographs against sketches, clip-art, or “photo-fits”.|
|Image cleansing||Automatic removal of unwanted parts of an image (e.g. background stars in photographs of nebulae).|
|Scene understanding||Identifying objects; identifying objects within objects; identifying errors in a scene; identifying image faults.|