Texture image classification using unsupervised learning

Nedyalko Petrov; Ivan Jordanov

Texture image classification using unsupervised learning

Nedyalko Petrov^*, Ivan Jordanov

^*Corresponding author for this work

School of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

We investigate further our intelligent machine vision system for pattern recognition and texture image classification. A database of about 335 texture images of industrial cork tiles is used for this research. The images need to be classified into several classes based on their texture features similarities. In this work, we assume that there is no a priori human vision expert knowledge about the classes. After pre-processing of the data, feature extraction and conducting statistical analysis by applying principal component analysis (PCA) and linear discriminant analysis (LDA), we investigate unsupervised neural network learning. Self-organizing map (SOM) neural networks are trained, tested and validated and the obtained results are discussed and critically compared with research works investigating similar approaches.

Original language	English
Title of host publication	Proceedings of the 11th IASTED International Conference on Artificial Intelligence and Applications, AIA 2011
Publisher	ACTA Press
Pages	29-35
Number of pages	7
ISBN (Print)	9780889868632
Publication status	Published - 2011
Event	11th IASTED International Conference on Artificial Intelligence and Applications, AIA 2011 - Innsbruck, Austria Duration: 14 Feb 2011 → 16 Feb 2011

Conference

Conference	11th IASTED International Conference on Artificial Intelligence and Applications, AIA 2011
Country/Territory	Austria
City	Innsbruck
Period	14/02/11 → 16/02/11

Keywords

Artificial neural networks
Feature extraction
Pattern recognition and classification
Self-organizing maps
Statistical data pre-processing
Unsupervised learning

Access to Document

https://www.iasted.org/conferences/pastinfo-717.html

Cite this

@inproceedings{d54cee83814f4db0bb407ce0da94536a,

title = "Texture image classification using unsupervised learning",

abstract = "We investigate further our intelligent machine vision system for pattern recognition and texture image classification. A database of about 335 texture images of industrial cork tiles is used for this research. The images need to be classified into several classes based on their texture features similarities. In this work, we assume that there is no a priori human vision expert knowledge about the classes. After pre-processing of the data, feature extraction and conducting statistical analysis by applying principal component analysis (PCA) and linear discriminant analysis (LDA), we investigate unsupervised neural network learning. Self-organizing map (SOM) neural networks are trained, tested and validated and the obtained results are discussed and critically compared with research works investigating similar approaches.",

keywords = "Artificial neural networks, Feature extraction, Pattern recognition and classification, Self-organizing maps, Statistical data pre-processing, Unsupervised learning",

author = "Nedyalko Petrov and Ivan Jordanov",

note = "publisher's website and DOI not loading - 10.2316/P.2011.717-114; 11th IASTED International Conference on Artificial Intelligence and Applications, AIA 2011 ; Conference date: 14-02-2011 Through 16-02-2011",

year = "2011",

language = "English",

isbn = "9780889868632",

pages = "29--35",

booktitle = "Proceedings of the 11th IASTED International Conference on Artificial Intelligence and Applications, AIA 2011",

publisher = "ACTA Press",

address = "Canada",

}

Petrov, N & Jordanov, I 2011, Texture image classification using unsupervised learning. in Proceedings of the 11th IASTED International Conference on Artificial Intelligence and Applications, AIA 2011. ACTA Press, pp. 29-35, 11th IASTED International Conference on Artificial Intelligence and Applications, AIA 2011, Innsbruck, Austria, 14/02/11. <https://www.iasted.org/conferences/pastinfo-717.html>

TY - GEN

T1 - Texture image classification using unsupervised learning

AU - Petrov, Nedyalko

AU - Jordanov, Ivan

N1 - publisher's website and DOI not loading - 10.2316/P.2011.717-114

PY - 2011

Y1 - 2011

N2 - We investigate further our intelligent machine vision system for pattern recognition and texture image classification. A database of about 335 texture images of industrial cork tiles is used for this research. The images need to be classified into several classes based on their texture features similarities. In this work, we assume that there is no a priori human vision expert knowledge about the classes. After pre-processing of the data, feature extraction and conducting statistical analysis by applying principal component analysis (PCA) and linear discriminant analysis (LDA), we investigate unsupervised neural network learning. Self-organizing map (SOM) neural networks are trained, tested and validated and the obtained results are discussed and critically compared with research works investigating similar approaches.

AB - We investigate further our intelligent machine vision system for pattern recognition and texture image classification. A database of about 335 texture images of industrial cork tiles is used for this research. The images need to be classified into several classes based on their texture features similarities. In this work, we assume that there is no a priori human vision expert knowledge about the classes. After pre-processing of the data, feature extraction and conducting statistical analysis by applying principal component analysis (PCA) and linear discriminant analysis (LDA), we investigate unsupervised neural network learning. Self-organizing map (SOM) neural networks are trained, tested and validated and the obtained results are discussed and critically compared with research works investigating similar approaches.

KW - Artificial neural networks

KW - Feature extraction

KW - Pattern recognition and classification

KW - Self-organizing maps

KW - Statistical data pre-processing

KW - Unsupervised learning

UR - http://www.scopus.com/inward/record.url?scp=79958163619&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:79958163619

SN - 9780889868632

SP - 29

EP - 35

BT - Proceedings of the 11th IASTED International Conference on Artificial Intelligence and Applications, AIA 2011

PB - ACTA Press

T2 - 11th IASTED International Conference on Artificial Intelligence and Applications, AIA 2011

Y2 - 14 February 2011 through 16 February 2011

ER -

Texture image classification using unsupervised learning

Abstract

Conference

Keywords

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