Open Access iconOpen Access

ARTICLE

Cluster Representation of the Structural Description of Images for Effective Classification

Yousef Ibrahim Daradkeh1,*, Volodymyr Gorokhovatskyi2, Iryna Tvoroshenko2, Medien Zeghid3,4

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: email

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

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.

Keywords


Cite This Article

APA Style
Daradkeh, Y.I., Gorokhovatskyi, V., Tvoroshenko, I., Zeghid, M. (2022). Cluster representation of the structural description of images for effective classification. Computers, Materials & Continua, 73(3), 6069-6084. https://doi.org/10.32604/cmc.2022.030254
Vancouver Style
Daradkeh YI, Gorokhovatskyi V, Tvoroshenko I, Zeghid M. Cluster representation of the structural description of images for effective classification. Comput Mater Contin. 2022;73(3):6069-6084 https://doi.org/10.32604/cmc.2022.030254
IEEE Style
Y.I. Daradkeh, V. Gorokhovatskyi, I. Tvoroshenko, and M. Zeghid, “Cluster Representation of the Structural Description of Images for Effective Classification,” Comput. Mater. Contin., vol. 73, no. 3, pp. 6069-6084, 2022. https://doi.org/10.32604/cmc.2022.030254



cc Copyright © 2022 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 1031

    View

  • 502

    Download

  • 0

    Like

Share Link