Open Access

ARTICLE

Cluster Representation of the Structural Description of Images for Effective Classification

by Yousef Ibrahim Daradkeh^1,*, Volodymyr Gorokhovatskyi², Iryna Tvoroshenko², Medien Zeghid^3,4

1 Department of Computer Engineering and Networks, College of Engineering at Wadi Addawasir, Prince Sattam Bin Abdulaziz University, Wadi Addawasir, 11991, Saudi Arabia
2 Department of Informatics, Kharkiv National University of Radio Electronics, Kharkiv, 61166, Ukraine
3 Electrical Engineering Department, College of Engineering at Wadi Addawasir, Prince Sattam Bin Abdulaziz University, Wadi Addawasir, 11991, Saudi Arabia
4 Electronics and Micro-Electronics Laboratory, Faculty of Sciences, University of Monastir, Monastir, 5000, Tunisia

* Corresponding Author: Yousef Ibrahim Daradkeh. Email:

Computers, Materials & Continua 2022, 73(3), 6069-6084. https://doi.org/10.32604/cmc.2022.030254

Received 22 March 2022; Accepted 18 May 2022; Issue published 28 July 2022

Abstract

The problem of image recognition in the computer vision systems is being studied. The results of the development of efficient classification methods, given the figure of processing speed, based on the analysis of the segment representation of the structural description in the form of a set of descriptors are provided. We propose three versions of the classifier according to the following principles: “object–etalon”, “object descriptor–etalon” and “vector description of the object–etalon”, which are not similar in level of integration of researched data analysis. The options for constructing clusters over the whole set of descriptions of the etalon database, separately for each of the etalons, as well as the optimal method to compare sets of segment centers for the etalons and object, are implemented. An experimental rating of the efficiency of the created classifiers in terms of productivity, processing time, and classification quality has been realized of the applied. The proposed methods classify the set of etalons without error. We have formed the inference about the efficiency of classification approaches based on segment centers. The time of image processing according to the developed methods is hundreds of times less than according to the traditional one, without reducing the accuracy.

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Keywords

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