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Darius Andriukaitis
Kaunas University of Technology, Lithuania

Radu Arsinte
Technical University of Cluj Napoca, Romania

Ivan Baronak
Slovak University of Technology, Slovakia

Khosrow Behbehani
The University of Texas at Arlington, United States

Mohamed El Hachemi Benbouzid
University of Brest, France

Dalibor Biolek
University of Defence, Czech Republic

Klara Capova
University of Zilina, Slovakia

Ray-Guang Cheng
National Taiwan University of Science and Technology, Taiwan, Province of China

Erik Chromy
UPC Broadband Slovakia, Slovakia

Milan Dado
University of Zilina, Slovakia

Petr Drexler
Brno University of Technology, Czech Republic

Eva Gescheidtova
Brno University of Technology, Czech Republic

Gokhan Hakki Ilk
Ankara University, Turkey

Janusz Jezewski
Institute of Medical Technology and Equipment, Poland

Rene Kalus
VSB - Technical University of Ostrava, Czech Republic

Ivan Kasik
Academy of Sciences of the Czech Republic, Czech Republic

Jan Kohout
University of Defence, Czech Republic

Ondrej Krejcar
University of Hradec Kralove, Czech Republic

Zbigniew Leonowicz
Wroclaw University of Science and Technology, Poland

Miroslaw Luft
Technical University of Radom, Poland

Stanislav Marchevsky
Technical University of Kosice, Slovakia

Jerzy Mikulski
University of Economics in Katowice, Katowice, Poland

Karol Molnar
Honeywell International, Czech Republic

Thang Trung Nguyen
Ton Duc Thang University, Viet Nam

Miloslav Ohlidal
Brno University of Technology, Czech Republic

Neeta Pandey
Delhi Technological University, India

Alex Noel Joseph Raj
Shantou University, China

Marek Penhaker
VSB - Technical University of Ostrava, Czech Republic

Wasiu Oyewole Popoola
The University of Edinburgh, United Kingdom

Roman Prokop
Tomas Bata University in Zlin, Czech Republic

Karol Rastocny
University of Zilina, Slovakia

Marie Richterova
University of Defence, Czech Republic

Gheorghe Sebestyen-Pal
Technical University of Cluj Napoca, Romania

Sergey Vladimirovich Serebriannikov
National Research University "MPEI", Russian Federation

Yuriy Shmaliy
Guanajuato University, Mexico

Vladimir Schejbal
University of Pardubice, Czech Republic

Bohumil Skala
University of West Bohemia in Plzen, Czech Republic

Lorand Szabo
Technical University of Cluj Napoca, Romania

Adam Szelag
Warsaw University of Technology, Poland

Ahmadreza Tabesh
Isfahan University of Technology, Iran, Islamic Republic Of

Mauro Tropea
DIMES Department of University of Calabria, Italy

Viktor Valouch
Academy of Sciences of the Czech Republic, Czech Republic

Jiri Vodrazka
Czech Technical University in Prague, Czech Republic

Miroslav Voznak
VSB - Technical University of Ostrava, Czech Republic

He Wen
Hunan University, China

Otakar Wilfert
Brno University of Technology, Czech Republic


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Search for Visual Objects by Request in the Form of a Cluster Representation for the Structural Image Description

Volodymyr Gorokhovatskyi, Iryna Tvoroshenko, Oleg Kobylin, Nataliia Vlasenko

DOI: 10.15598/aeee.v21i1.4661


Abstract

The key task of computer vision is the recognition of visual objects in the analysed image. This paper proposes a method of searching for objects in an image, based on the identification of a cluster representation of the query descriptions and the current image of the window with the calculation of the relevance measure. The implementation of a cluster representation significantly increases the speed of identification or classification of visual objects while maintaining a sufficient level of accuracy. Based on the development of models for the analysis and processing of a set of descriptors of keypoints, we have obtained an effective method for the identification of visual objects. A comparative experiment with the traditional method has been conducted, where a linear search for the nearest descriptor was implemented for identification without using a cluster representation of the description. In the experiment, a speed gain for the developed method has been obtained in comparison with the traditional one by approximately 5.2 times with the same level of accuracy. The method can be used in applied tasks where the time of object identification is critical. The developed method can be applied to search for several objects of different classes. The effectiveness of the method can be increased by varying the values of its parameters and adapting to the characteristics of the data.

Keywords


Computer vision; detector; Hamming metric; k-means method.

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