Abstract
The accurate classification of iron ores is one of the challenging tasks in the mining industry, and at the same time, it is essential for the future destination of the ores. The present study attempts to develop a machine-vision-based system, combining feature extraction, feature selection, and image classification, in order to predict the class of the iron ores. A total of 280 image features were extracted from each of the captured images of iron ores. The feature includes 10 statistical features for each component of the six colour spaces (RGB, HSI, XYZ, CMYK, Lab, and Grey) and four frequency-transformed components (DCT, DFT, DWT, and Gabor filter). In order to select the optimum feature subset for the model development, a sequential forward floating selection (SFFS) algorithm was used. The optimum feature contains only 3% of the total number of features. The support vector machine (SVM) algorithm was used for the development classification model. The four confusion matrix parameters (sensitivity, specificity, accuracy, and misclassification) along with the Q-statistics and the correlation were used as performance criteria for the model classification and were found to be 0.9792, 0.9949, 0.9918, 0.0082, 0.9999, and 0.9695, respectively. The high value of sensitivity, specificity, and accuracy and the low value of the misclassification indicate a good performance of the model. The performance of the model was also compared with other classification algorithms (k-nearest neighbours, classification tree, discriminant classifier, naïve Bayes). It was observed that the proposed algorithm performs reasonably well in terms of categorising the classes of iron ore.
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The study was conducted at the NIT Rourkela, Odisha, India. The authors are thankful to the authorities of the Gua mine and Tensa mine for giving the permission to collect the iron ore sample.
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Patel, A.K., Chatterjee, S. & Gorai, A.K. Development of an expert system for iron ore classification. Arab J Geosci 11, 401 (2018). https://doi.org/10.1007/s12517-018-3733-x
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DOI: https://doi.org/10.1007/s12517-018-3733-x