Information-Theoretic Approach to the Problem of Automatic Image Recognition

The problem of automatic half-tone image recognition from the large database is formulated. Its decision using data on a problem of statistical classification of discrete objects on the basis of the minimum information discrimination principle is proposed. The new probabilistic model of a half-tone image is studied. On the basis of this model, a new image recognition algorithm using the minimum Kullback–Leibler information discrimination criterion is synthesized. It is proved that this criterion is optimal in Bayesian terms for the classification problem of discrete random objects, including digital images. The program and results of experimental research on the face recognition problem are presented. It is shown that the accuracy of image recognition with various light conditions on the basis of the offered criterion is essentially higher than the accuracy of traditional methods. © 2022, Springer Science+Business Media, LLC, part of Springer Nature.