Computer Vision: Shape Classification via Explainable AI

A central problem in computer vision is to compare shapes and assess how similar they are. This is used for instance in text recognition. Modern techniques involve neural networks. In this article, I revisit a methodology designed in 1914, before computer even existed. It leads to an efficient, automated AI algorithm. The benefit is that the decision process made by this black-box system, can be explained (almost) in layman’s terms, and thus easily controlled.

To the contrary, neural networks use millions of weights that are impossible to interpret, potentially leading to over-fitting. Why they work very well on some data and no so well on other data is a mystery. My “old-fashioned” classifier, adapted to modern data and computer architectures, lead to full control of the parameters. In other words, you know beforehand how fine-tuning the parameters will impact the output. Thus the word explainable AI.

In an ideal world, one would want to blend both methods, to benefit from their respective strengths, and minimize their respective drawbacks.

Read the full article, here.