Multiclass classification of texture images using greedy feature selection algorithms

We investigated four greedy algorithms for selecting the most informative features for solving the problem of multiclass classification. The algorithms have been experimentally tested on images from the Kylberg Texture Dataset [1]. The formation of features was carried out using the MaZda software,...

Full description

Saved in:
Bibliographic Details
Main Authors: Konevsky, Vladislav, Gaidel, Andrey, Asatryan, David, Paringer, Rustam, Kupriyanov, Alexander, Haroutunian, Mariam
Format: Conference Proceeding
Language:English
Subjects:
Greedy algorithms
Image classification
Texture
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We investigated four greedy algorithms for selecting the most informative features for solving the problem of multiclass classification. The algorithms have been experimentally tested on images from the Kylberg Texture Dataset [1]. The formation of features was carried out using the MaZda software, which allows calculating the texture characteristics of the image. With the help of the algorithm of greedy forward selection, it was possible to reduce the dimension of the feature space from 298 to 141 features, and the proportion of correctly classified objects increased from 85% to 96%.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0136507