An investigation of machine learning method based on fractal compression

E. Minaev
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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.
基于分形压缩的机器学习方法研究
本文研究了基于循环分形编码的机器学习方法和适用于移动平台的领域块字典的使用,并对分形图像的性能和存储量进行了优化。该方法的主要思想是使用基于迭代函数系统的分形压缩方法对原始图像进行降维,并使用循环分形编码来表示图像的类别。研究结果表明,该方法在MSTAR上的正确识别率平均为0.892,识别时间平均为254 ms。所取得的结果可用于移动平台,包括无人机和地面自主机器人。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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