Open AccessApplication of multi-component data model for class descriptions in the image classification problem
Author(s) -
Volodymyr Gorokhovatskyi,
Natalia Stiahlyk,
Oleksii Zhadan
Publication year - 2022
Publication title -
sučasnì ìnformacìjnì sistemi
Language(s) - English
Resource type - Journals
ISSN - 2522-9052
DOI - 10.20998/2522-9052.2022.1.01
Subject(s) - computer science , set (abstract data type) , data mining , contextual image classification , one class classification , metric (unit) , software , relevance (law) , data set , artificial intelligence , pattern recognition (psychology) , support vector machine , image (mathematics) , machine learning , operations management , political science , law , economics , programming language
The subject of research of the article is the methods of image classification according to the set of descriptors of key points in computer vision systems. The aim is to increase the efficiency of classification by introducing a multicomponent data model on a set of descriptors for the base of reference images. Applied methods: ORB detector and descriptors, apparatus of set theory and vector space, metric models for determining the relevance of sets of multidimensional vectors, elements of probability theory, software modeling. Results are obtained: a modified method of image classification based on the introduction of a multicomponent model for data analysis with a system of centers is developed, methods of constructing a set of data centers are identified, the most effective is the set medoid and centers based on it. The effectiveness of the modification significantly depends on the method of forming the centers, the applied classification model, as well as on the data itself. The best results were shown by the classification with the integrated indicator separately for each of the standards in the form of the sum of the values of the distributions for the set of centers; experimentally tested the effectiveness of the classification, confirmed the efficiency of the proposed method. The practical significance of the work is the construction of classification models in the transformed data space, confirmation of the efficiency of the proposed modifications on the examples of images, the creation of software for the implementation of developed classification methods in computer vision systems.