Our approach

Our algorithms create a “numerical fingerprint” that represents the attributes of an individual frame of a video. Then, the algorithm matches the fingerprint with similar fingerprints in the content library.

What constitutes a match? Well, it depends. This is what happens behind the scenes to deal with the subjective nature of determining a match. Attrasoft’s neural network is taught what a match is by feeding in a sample set of image pairs. An image pair is two images of the same thing but the images differ slightly. By feeding the neural network filter with a series of image pairs, the filter learns the similarities and differences so it can come to understand what you mean by a match.

Because our algorithms reduce the large amount of data in a video into a small numerical fingerprint – the image is represented by a string of numbers – the video matching process can be performed in RAM. This makes the matching process very fast.

The algorithms have been refined in large scale implementations. As a result, we can achieve levels of accuracy that are unmatched in the industry.

Speed and accuracy. That’s what sets our approach apart.

How it works

For you deep math folks, our algorithms are based on Markov Chain Theory and Neural Network Theory. Images are classified by a distribution function that describes the invariant distribution that the Markov chain settles on.

Image Recognition

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