Using a convenient AJAX-based interface, ISIS 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, ISIS 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.
Recognition vs. Similarity
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Using a normal web-browser, a query image is submitted to the server and the results returned in the main panel.
The Intesym idemetric processor used by the server can be either a software-based implementation or a hardware-based Cortica unit.
In the image to the right, a picture of three golden dragons in front of a hedge is submitted. From the database the most similar images are found as being a copy of the submitted image (best match), a photograph of a single dragon (second best match), and a photograph of a tree hedge (third best match). Other photographs in the database were quite different to these and so followed with lower probabilities of similarity.
In the image to the right, a picture of a spiral galaxy is submitted. The best results are shown to be primarily spiral galaxies, with elliptical shapes following. The database contained many images of galaxies, clusters, and nebulae. It demonstrates the ability of the system to perform purely visual taxonomic discrimination, where the results accord with the category of the scene instead of being ranked by the similarity of detail.
An image processing technology capable of, amongst other things, measuring the similarity of images, recognising contents, and understanding scenes.
Idemetric Processing homepage
Receptive Field networks
ISIS Similarity Server