Pattern recognition at different scales: A statistical perspective

•We introduce two typical statistical scales affecting the performance of a pattern recognition algorithm.•We describe the dependence of such scales on the image resolution.•We recover the results of the statistical analysis by using Support Vector Machines algorithm.•We discuss the effect of averaging the performance of a Support Vector Machine over different training samples.

In this paper we borrow concepts from Information Theory and Statistical Mechanics to perform a pattern recognition procedure on a set of X-ray hazelnut images. We identify two relevant statistical scales, whose ratio affects the performance of a machine learning algorithm based on statistical observables, and discuss the dependence of such scales on the image resolution. Finally, by averaging the performance of a Support Vector Machines algorithm over a set of training samples, we numerically verify the predicted onset of an “optimal” scale of resolution, at which the pattern recognition is favoured.