Analysis of GLCM Parameters for Textures Classification on UMD Database Images

Alsadegh Mohamed, Zhongyu Lu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Texture analysis is one of the most important techniques that have been used in image processing for many purposes, including image classification. The texture determines the region of a given gray level image, and reflects its relevant information. Several methods of analysis have been invented and developed to deal with texture in recent years, and each one has its own method of extracting features from the texture. These methods can be divided into two main approaches: statistical methods and processing methods. Gray Level Co-occurrence Matrix (GLCM) is the most popular statistical method used to get features from the texture. In addition to GLCM, a number of equations of Haralick characteristics will be used to calculate values used as discriminate features among different images in this study. There are many parameters of GLCM that should be taken into consideration to increase the discrimination between images belonging to different classes. In this study, we aim to evaluate GLCM parameters. For three decades now, GLCM is popular method used for texture analysis. Neural network which is one of supervised methods will also be used as a classifier. And finally, the database for this study will be images prepared from UMD (University of Maryland database).
LanguageEnglish
Title of host publicationProceedings of The Fifth International Conference on Advanced Communications and Computation
Subtitle of host publicationINFOCOMP 2015
Place of PublicationBrussels, Belgium
PublisherInternational Academy, Research, and Industry Association (IARIA)
Pages111-116
Number of pages6
ISBN (Electronic)9781612084169
Publication statusPublished - 21 Jun 2015
EventFifth International Conference on Advanced Communications and Computation - Brussels, Belgium
Duration: 21 Jun 201526 Jun 2015
https://www.iaria.org/conferences2015/INFOCOMP15.html (Link to Conference Website)

Conference

ConferenceFifth International Conference on Advanced Communications and Computation
Abbreviated titleINFOCOMP 2015
CountryBelgium
CityBrussels
Period21/06/1526/06/15
Internet address

Fingerprint

Textures
Statistical methods
Image classification
Image processing
Classifiers
Neural networks
Processing

Cite this

Mohamed, A., & Lu, Z. (2015). Analysis of GLCM Parameters for Textures Classification on UMD Database Images. In Proceedings of The Fifth International Conference on Advanced Communications and Computation: INFOCOMP 2015 (pp. 111-116). Brussels, Belgium: International Academy, Research, and Industry Association (IARIA).
Mohamed, Alsadegh ; Lu, Zhongyu. / Analysis of GLCM Parameters for Textures Classification on UMD Database Images. Proceedings of The Fifth International Conference on Advanced Communications and Computation: INFOCOMP 2015. Brussels, Belgium : International Academy, Research, and Industry Association (IARIA), 2015. pp. 111-116
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title = "Analysis of GLCM Parameters for Textures Classification on UMD Database Images",
abstract = "Texture analysis is one of the most important techniques that have been used in image processing for many purposes, including image classification. The texture determines the region of a given gray level image, and reflects its relevant information. Several methods of analysis have been invented and developed to deal with texture in recent years, and each one has its own method of extracting features from the texture. These methods can be divided into two main approaches: statistical methods and processing methods. Gray Level Co-occurrence Matrix (GLCM) is the most popular statistical method used to get features from the texture. In addition to GLCM, a number of equations of Haralick characteristics will be used to calculate values used as discriminate features among different images in this study. There are many parameters of GLCM that should be taken into consideration to increase the discrimination between images belonging to different classes. In this study, we aim to evaluate GLCM parameters. For three decades now, GLCM is popular method used for texture analysis. Neural network which is one of supervised methods will also be used as a classifier. And finally, the database for this study will be images prepared from UMD (University of Maryland database).",
keywords = "GLCM parameters, Haralick Feature Extraction, texture classification, Texture Classification using window size",
author = "Alsadegh Mohamed and Zhongyu Lu",
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Mohamed, A & Lu, Z 2015, Analysis of GLCM Parameters for Textures Classification on UMD Database Images. in Proceedings of The Fifth International Conference on Advanced Communications and Computation: INFOCOMP 2015. International Academy, Research, and Industry Association (IARIA), Brussels, Belgium, pp. 111-116, Fifth International Conference on Advanced Communications and Computation, Brussels, Belgium, 21/06/15.

Analysis of GLCM Parameters for Textures Classification on UMD Database Images. / Mohamed, Alsadegh; Lu, Zhongyu.

Proceedings of The Fifth International Conference on Advanced Communications and Computation: INFOCOMP 2015. Brussels, Belgium : International Academy, Research, and Industry Association (IARIA), 2015. p. 111-116.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AU - Lu, Zhongyu

PY - 2015/6/21

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N2 - Texture analysis is one of the most important techniques that have been used in image processing for many purposes, including image classification. The texture determines the region of a given gray level image, and reflects its relevant information. Several methods of analysis have been invented and developed to deal with texture in recent years, and each one has its own method of extracting features from the texture. These methods can be divided into two main approaches: statistical methods and processing methods. Gray Level Co-occurrence Matrix (GLCM) is the most popular statistical method used to get features from the texture. In addition to GLCM, a number of equations of Haralick characteristics will be used to calculate values used as discriminate features among different images in this study. There are many parameters of GLCM that should be taken into consideration to increase the discrimination between images belonging to different classes. In this study, we aim to evaluate GLCM parameters. For three decades now, GLCM is popular method used for texture analysis. Neural network which is one of supervised methods will also be used as a classifier. And finally, the database for this study will be images prepared from UMD (University of Maryland database).

AB - Texture analysis is one of the most important techniques that have been used in image processing for many purposes, including image classification. The texture determines the region of a given gray level image, and reflects its relevant information. Several methods of analysis have been invented and developed to deal with texture in recent years, and each one has its own method of extracting features from the texture. These methods can be divided into two main approaches: statistical methods and processing methods. Gray Level Co-occurrence Matrix (GLCM) is the most popular statistical method used to get features from the texture. In addition to GLCM, a number of equations of Haralick characteristics will be used to calculate values used as discriminate features among different images in this study. There are many parameters of GLCM that should be taken into consideration to increase the discrimination between images belonging to different classes. In this study, we aim to evaluate GLCM parameters. For three decades now, GLCM is popular method used for texture analysis. Neural network which is one of supervised methods will also be used as a classifier. And finally, the database for this study will be images prepared from UMD (University of Maryland database).

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Mohamed A, Lu Z. Analysis of GLCM Parameters for Textures Classification on UMD Database Images. In Proceedings of The Fifth International Conference on Advanced Communications and Computation: INFOCOMP 2015. Brussels, Belgium: International Academy, Research, and Industry Association (IARIA). 2015. p. 111-116