Classification of Marble Using Image Processing

Fisha Haileslassie; Adane Leta; Gizatie Desalegn; Meles Kalayu

doi:doi:10.11648/j.ijdst.20190503.11

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Classification of Marble Using Image Processing

Fisha Haileslassie, Adane Leta, Gizatie Desalegn, Meles Kalayu

Received: 13 November 2019 Accepted: 18 December 2019 Published: 24 December 2019

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Abstract

Classification of marble image according to usage purpose and quality is an important procedure for export. Discrimination between marble varieties is a difficult task during selection, since it requires trainings and experience. Therefore, the development of automatic prediction model based on image processing is a potential application area to support experts across the world. In this study an attempt has been made to develop marble variety classification model by comparing color, texture and ensemble of color and texture. In view of this, a digital image processing technique based on combined texture and color features have been explored good classification performance to classify varieties of marble image. On the average 60 images were taken from each of the three marble varieties (Grade A, Grade B, Grade C). The total number of images taken was 180. For the classification model we applied image preprocessing techniques; image acquisition, image conversion, noise removal, image enhancement, edge detection and image binarization. For texture extraction gray level co-occurrence matrix, for color extraction color histogram was applied. For classification five textures and six color features were extracted from each marble image. To build the classification models for prediction of marble varieties, K-Nearest Neighbors (KNN), Artificial Neural Network (ANN) are investigated. Based on experimental results, ANN outperforms KNN. Quantitatively, an average accuracy of 83.3% and 93.7% is achieved KNN and ANN respectively for Grade A, Grade B, Grade C varieties with the combined feature sets of color and texture. This shows an encouraging result to design an applicable marble classification model. Marble fractured and vines of the images affect greatly the performance of the classifier and hence they are the future research direction that needs an investigation of generic noise removal and feature extraction techniques.

DOI	10.11648/j.ijdst.20190503.11
Published in	International Journal on Data Science and Technology (Volume 5, Issue 3, September 2019)
Page(s)	57-65
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Classification of Marbles, Feature Extraction, Artificial Neural Network Classifier, K-nearest Neighbor, Sobel Edge Detector

References

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[9]	P. J. R. C. Sousa J. M. C., “Comparison of intelligent classification techniques applied to marble classification,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 3212, pp. 802–809, 2004.
[10]	J. Miguel and P. Batista, “Marble Polished Stones Automatic Classification,” pp. 1–8, 2015.
[11]	E. Symposium, A. Neural, and N. Bruges, “Marble Slabs Quality Classification System using Texture Recognition and Neural Networks Methodology,” no. April, pp. 75–80, 1999.
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[13]	I. B. Gundogdu, “Color identification of some Turkish marbles,” vol. 22, pp. 1342–1349, 2008.
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APA Style

Fisha Haileslassie, Adane Leta, Gizatie Desalegn, Meles Kalayu. (2019). Classification of Marble Using Image Processing. International Journal on Data Science and Technology, 5(3), 57-65. https://doi.org/10.11648/j.ijdst.20190503.11

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ACS Style

Fisha Haileslassie; Adane Leta; Gizatie Desalegn; Meles Kalayu. Classification of Marble Using Image Processing. Int. J. Data Sci. Technol. 2019, 5(3), 57-65. doi: 10.11648/j.ijdst.20190503.11

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AMA Style

Fisha Haileslassie, Adane Leta, Gizatie Desalegn, Meles Kalayu. Classification of Marble Using Image Processing. Int J Data Sci Technol. 2019;5(3):57-65. doi: 10.11648/j.ijdst.20190503.11

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@article{10.11648/j.ijdst.20190503.11,
  author = {Fisha Haileslassie and Adane Leta and Gizatie Desalegn and Meles Kalayu},
  title = {Classification of Marble Using Image Processing},
  journal = {International Journal on Data Science and Technology},
  volume = {5},
  number = {3},
  pages = {57-65},
  doi = {10.11648/j.ijdst.20190503.11},
  url = {https://doi.org/10.11648/j.ijdst.20190503.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdst.20190503.11},
  abstract = {Classification of marble image according to usage purpose and quality is an important procedure for export. Discrimination between marble varieties is a difficult task during selection, since it requires trainings and experience. Therefore, the development of automatic prediction model based on image processing is a potential application area to support experts across the world. In this study an attempt has been made to develop marble variety classification model by comparing color, texture and ensemble of color and texture. In view of this, a digital image processing technique based on combined texture and color features have been explored good classification performance to classify varieties of marble image. On the average 60 images were taken from each of the three marble varieties (Grade A, Grade B, Grade C). The total number of images taken was 180. For the classification model we applied image preprocessing techniques; image acquisition, image conversion, noise removal, image enhancement, edge detection and image binarization. For texture extraction gray level co-occurrence matrix, for color extraction color histogram was applied. For classification five textures and six color features were extracted from each marble image. To build the classification models for prediction of marble varieties, K-Nearest Neighbors (KNN), Artificial Neural Network (ANN) are investigated. Based on experimental results, ANN outperforms KNN. Quantitatively, an average accuracy of 83.3% and 93.7% is achieved KNN and ANN respectively for Grade A, Grade B, Grade C varieties with the combined feature sets of color and texture. This shows an encouraging result to design an applicable marble classification model. Marble fractured and vines of the images affect greatly the performance of the classifier and hence they are the future research direction that needs an investigation of generic noise removal and feature extraction techniques.},
 year = {2019}
}

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TY - JOUR
T1 - Classification of Marble Using Image Processing
AU - Fisha Haileslassie
AU - Adane Leta
AU - Gizatie Desalegn
AU - Meles Kalayu
Y1 - 2019/12/24
PY - 2019
N1 - https://doi.org/10.11648/j.ijdst.20190503.11
DO - 10.11648/j.ijdst.20190503.11
T2 - International Journal on Data Science and Technology
JF - International Journal on Data Science and Technology
JO - International Journal on Data Science and Technology
SP - 57
EP - 65
PB - Science Publishing Group
SN - 2472-2235
UR - https://doi.org/10.11648/j.ijdst.20190503.11
AB - Classification of marble image according to usage purpose and quality is an important procedure for export. Discrimination between marble varieties is a difficult task during selection, since it requires trainings and experience. Therefore, the development of automatic prediction model based on image processing is a potential application area to support experts across the world. In this study an attempt has been made to develop marble variety classification model by comparing color, texture and ensemble of color and texture. In view of this, a digital image processing technique based on combined texture and color features have been explored good classification performance to classify varieties of marble image. On the average 60 images were taken from each of the three marble varieties (Grade A, Grade B, Grade C). The total number of images taken was 180. For the classification model we applied image preprocessing techniques; image acquisition, image conversion, noise removal, image enhancement, edge detection and image binarization. For texture extraction gray level co-occurrence matrix, for color extraction color histogram was applied. For classification five textures and six color features were extracted from each marble image. To build the classification models for prediction of marble varieties, K-Nearest Neighbors (KNN), Artificial Neural Network (ANN) are investigated. Based on experimental results, ANN outperforms KNN. Quantitatively, an average accuracy of 83.3% and 93.7% is achieved KNN and ANN respectively for Grade A, Grade B, Grade C varieties with the combined feature sets of color and texture. This shows an encouraging result to design an applicable marble classification model. Marble fractured and vines of the images affect greatly the performance of the classifier and hence they are the future research direction that needs an investigation of generic noise removal and feature extraction techniques.
VL - 5
IS - 3
ER -

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Author Information

Fisha Haileslassie

Department of Computer Science, Faculty of Technology, Debre Tabor University, Debre Tabor, Ethiopia
Adane Leta

Colleage of Informatics, University of Gondar, Gondar, Ethiopia
Gizatie Desalegn

Department of Computer Science, Faculty of Technology, Debre Tabor University, Debre Tabor, Ethiopia
Meles Kalayu

Department Information Technology, Raya University, Maichew, Ethiopia

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APA Style

Fisha Haileslassie, Adane Leta, Gizatie Desalegn, Meles Kalayu. (2019). Classification of Marble Using Image Processing. International Journal on Data Science and Technology, 5(3), 57-65. https://doi.org/10.11648/j.ijdst.20190503.11

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ACS Style

Fisha Haileslassie; Adane Leta; Gizatie Desalegn; Meles Kalayu. Classification of Marble Using Image Processing. Int. J. Data Sci. Technol. 2019, 5(3), 57-65. doi: 10.11648/j.ijdst.20190503.11

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AMA Style

Fisha Haileslassie, Adane Leta, Gizatie Desalegn, Meles Kalayu. Classification of Marble Using Image Processing. Int J Data Sci Technol. 2019;5(3):57-65. doi: 10.11648/j.ijdst.20190503.11

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@article{10.11648/j.ijdst.20190503.11,
  author = {Fisha Haileslassie and Adane Leta and Gizatie Desalegn and Meles Kalayu},
  title = {Classification of Marble Using Image Processing},
  journal = {International Journal on Data Science and Technology},
  volume = {5},
  number = {3},
  pages = {57-65},
  doi = {10.11648/j.ijdst.20190503.11},
  url = {https://doi.org/10.11648/j.ijdst.20190503.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdst.20190503.11},
  abstract = {Classification of marble image according to usage purpose and quality is an important procedure for export. Discrimination between marble varieties is a difficult task during selection, since it requires trainings and experience. Therefore, the development of automatic prediction model based on image processing is a potential application area to support experts across the world. In this study an attempt has been made to develop marble variety classification model by comparing color, texture and ensemble of color and texture. In view of this, a digital image processing technique based on combined texture and color features have been explored good classification performance to classify varieties of marble image. On the average 60 images were taken from each of the three marble varieties (Grade A, Grade B, Grade C). The total number of images taken was 180. For the classification model we applied image preprocessing techniques; image acquisition, image conversion, noise removal, image enhancement, edge detection and image binarization. For texture extraction gray level co-occurrence matrix, for color extraction color histogram was applied. For classification five textures and six color features were extracted from each marble image. To build the classification models for prediction of marble varieties, K-Nearest Neighbors (KNN), Artificial Neural Network (ANN) are investigated. Based on experimental results, ANN outperforms KNN. Quantitatively, an average accuracy of 83.3% and 93.7% is achieved KNN and ANN respectively for Grade A, Grade B, Grade C varieties with the combined feature sets of color and texture. This shows an encouraging result to design an applicable marble classification model. Marble fractured and vines of the images affect greatly the performance of the classifier and hence they are the future research direction that needs an investigation of generic noise removal and feature extraction techniques.},
 year = {2019}
}

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