An investigation of machine learning method based on fractal compression

Abstract

In this article the method of machine learning with cyclic fractal coding and the use of domain block dictionary, adapted for use on mobile platforms, with optimization of performance and volume of stored fractal images is investigated. The main idea of the method is to use the fractal compression method based on iterated function systems to reduce the dimension of the original images, and to use cyclic fractal coding to represent the class of images. As a result of research of the method it was found that the share of correctly recognized objects on MSTAR averages 0.892, the recognition time averages 254 ms. The achieved results are acceptable for use in mobile platforms, including UAVs and ground autonomous robots.