Open Access

ARTICLE

Classification of Images Based on a System of Hierarchical Features

by Yousef Ibrahim Daradkeh¹, Volodymyr Gorokhovatskyi², Iryna Tvoroshenko^2,*, Mujahed Al-Dhaifallah³

1 Department of Computer Engineering and Networks, College of Engineering at Wadi Addawasir, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11991, Saudi Arabia
2 Department of Informatics, Kharkiv National University of Radio Electronics, Kharkiv, 61166, Ukraine
3 Control and Instrumentation Engineering Department, and Interdisciplinary Research Center (IRC) for Renewable Energy and Power Systems, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia

* Corresponding Author: Iryna Tvoroshenko. Email:

Computers, Materials & Continua 2022, 72(1), 1785-1797. https://doi.org/10.32604/cmc.2022.025499

Received 26 November 2021; Accepted 14 January 2022; Issue published 24 February 2022

Abstract

The results of the development of the new fast-speed method of classification images using a structural approach are presented. The method is based on the system of hierarchical features, based on the bitwise data distribution for the set of descriptors of image description. The article also proposes the use of the spatial data processing apparatus, which simplifies and accelerates the classification process. Experiments have shown that the time of calculation of the relevance for two descriptions according to their distributions is about 1000 times less than for the traditional voting procedure, for which the sets of descriptors are compared. The introduction of the system of hierarchical features allows to further reduce the calculation time by 2–3 times while ensuring high efficiency of classification. The noise immunity of the method to additive noise has been experimentally studied. According to the results of the research, the marginal degree of the hierarchy of features for reliable classification with the standard deviation of noise less than 30 is the 8-bit distribution. Computing costs increase proportionally with decreasing bit distribution. The method can be used for application tasks where object identification time is critical.

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Keywords

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