Classification of images based on the formation of independent cluster system within the structural descriptions of etalon dataset

Authors

  • Volodymyr Gorokhovatsky Kharkiv National University of RadioElectronics, Kharkiv http://orcid.org/0000-0002-7839-6223
  • Roman Ponomarenko Kharkiv National University of RadioElectronics, Kharkiv

DOI:

https://doi.org/10.20998/2522-9052.2020.2.04

Keywords:

computer vision, structural image recognition, key point descriptor, BRISK detector, clustering, k-means method, cluster center, description relevance, software modeling, image database

Abstract

The subject of this article is the structural methods for image classification in the space of images as a set of descriptors of key points for recognizing visual objects in computer vision systems. The goal is the creation of an effective classification method based on the embedding of a system of independent clusters for the etalon dataset. Task: the development of classification models in the newly created space of images, analysis of their computational efficiency, the evaluation of classification efficiency with software modeling. The methods are: BRISK detector for generating key point descriptors, data mining, k-means method for data clustering, software modeling. The following results were obtained: models for classifying object descriptions based on a system of independent clusters and their centers are proposed that simplify data processing and increase implementation speed, a comparative analysis of the developed methods with known methods was performed. The software implementation of the embedded classification models has been performed, an experiment to explore their effectiveness and evaluate the processing time has been conducted. Conclusions. The contribution of the research is the development of an image classification method based on the implementation of a system of independent clusters for reference descriptions, which contributes to an in-depth data analysis. The method has been implemented in modifications of cluster representation matching and based on competitive analysis of descriptors. The practical importance of the work is the constructing of the classification models in the created data space, confirming the efficiency of the proposed modifications to data processing, developing software models for implementing methods in computer vision systems.

Author Biographies

Volodymyr Gorokhovatsky, Kharkiv National University of RadioElectronics, Kharkiv

Doctor of Technical Sciences, Professor, Professor of Computer Science Department

Roman Ponomarenko, Kharkiv National University of RadioElectronics, Kharkiv

Doctoral Student of Computer Science Department

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Issue

Section

Identification problems in information systems