International Journal on Data Science and Technology
Volume 5, Issue 3, September 2019, Pages: 57-65
Received: Nov. 13, 2019;
Accepted: Dec. 18, 2019;
Published: Dec. 24, 2019
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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
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.
Classification of Marble Using Image Processing, International Journal on Data Science and Technology.
Vol. 5, No. 3,
2019, pp. 57-65.
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