A family of recognition algorithms based on the construction of k-dimensional threshold rules
Abstract
In this article, the authors proposed a family of recognition algorithms based on k-dimensional threshold rules and focused on the object recognition presented in the feature space of high dimension. These recognition algorithms are based on the concept of forming a set of k-dimensional preferred threshold rules that define an extremal algorithm within the proposed family of algorithms. A structural description of the developed family of recognition algorithms is given, presented as a sequence of computational procedures. The main ones are the procedures for determining a set of representative features; discriminant functions for each class; normalizing transformations; groups of highly correlated discriminant functions; basic discriminant functions; a set of preferred discriminant functions; a generalized recognition operator based on preferred discriminant functions. The results of experimental studies are presented; they showed that an increase in the accuracy of recognition results is provided (in comparison with known analogs) when using the indicated family of recognition algorithms. The main advantage of the proposed recognition algorithms is not only an increase in accuracy but also a significant reduction in arithmetic operations in object recognition. This property of the algorithms makes it possible to create various software recognition systems that operate in real time.