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Classification of Marble Using Image Processing

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.

Published in International Journal on Data Science and Technology (Volume 5, Issue 3)
DOI 10.11648/j.ijdst.20190503.11
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), 2019. 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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[3] Ö. Akkoyun, “An evaluation of image processing methods applied to marble quality classification,” 2nd Int. Conf. Comput. Technol. Dev., pp. 158–162, 2010.
[4] B. Jähne, Practical handbook on image processing for scientific and technical applications. 2004.
[5] B. H. BELAY, “Application of Image Processing Techniques for Malt-Barley Seed Identification,” Bahir Dar, Ethiop., 2015.
[6] E. Ardalı, “Classification of Marble Textures using Neural Networks and Image Processing Methods,” p. 41, 2008.
[7] A. P. D. O. AKKOYUN, “An evaluation of image processing methods applied to marble quality classification,” no. January, 2014.
[8] T. Seemann, “Digital Image Processing using Local Segmentation,” Philosophy, no. April, pp. 1–300, 2002.
[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.
[12] M. kora. B. Deviren Murat, “A Feature Extraction Method for Marble Tile Classification *,” vol. 7, pp. 0–3.
[13] I. B. Gundogdu, “Color identification of some Turkish marbles,” vol. 22, pp. 1342–1349, 2008.
[14] S. Gupta and S. G. Mazumdar, “Sobel Edge Detection Algorithm,” Internatoinal J. Comput. Sciennce Manag. Res., vol. 2, no. 2, pp. 1578–1583, 2013.
[15] E. J. Leavline, “Artificial Neural Network Design Flow for Classification Problem Using MATLAB,” no. December, 2015.
[16] P. Kamavisdar, S. Saluja, and S. Agrawal, “A Survey on Image Classification Approaches and Techniques,” vol. 2, no. 1, pp. 1005–1009, 2013.
[17] M. S. Nixon and A. S. Aguado, “Feature Extraction and Image Processing,” Acad. Press, p. 88, 2008.
[18] E. I. J. R., Parker, Kostas Terzidis, Algorithms for Image Processing and Computer Processing on Vision Second Edition ed. India, Wiley Publishing, 2011.
[19] M. K. and J. Fridrich, “On Detection of Median Filtering in Digital Images,” SPIE Conf. Media Forensics Secur., 2010.
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Cite This Article
  • 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
  • Department of Computer Science, Faculty of Technology, Debre Tabor University, Debre Tabor, Ethiopia

  • Colleage of Informatics, University of Gondar, Gondar, Ethiopia

  • Department of Computer Science, Faculty of Technology, Debre Tabor University, Debre Tabor, Ethiopia

  • Department Information Technology, Raya University, Maichew, Ethiopia

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