Abstract
In view of the complicacy and the diversity of inertinite macerals of coal, a classification method based on linear discriminate analysis (LDA) and support vector machine (SVM) is proposed. Firstly, according to differences of texture and intensity among macerals, inertinite macerals are represented with texture related features as energy, entropy, moment, local smooth and intensity related features as contrast, mean, standard deviation, 3-order moment deviation. Then, by using LDA, the initial features are further extracted by means of maximizing the inter-class dispersion as well as minimizing intra-class scatter. Finally, a classifier based on SVM with radial basis function (RBF-SVM) is built for the automatic classification of inertinite macerals. Experimental results show that, textures employed in this paper can present inertinite macerals effectively; with LDA and RBF-SVM, the dimension of feature space is reduced and the accuracy of classification is increased obviously.
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Acknowledgement
This work was supported by the National Natural Science Foundation of China (number 51574004); Natural Science Foundation of the Higher Education Institutions of Anhui Province, China (number KJ2019A0085); Academic Foundation for Top Talents of the Higher Education Institutions of Anhui Province, China (number gxbjZD2016041).
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Xue, Z., Cao, J., Wang, P., Yin, Z., Zhang, D. (2021). An LDA and RBF-SVM Based Classification Method for Inertinite Macerals of Coal. In: Peng, Y., Hu, SM., Gabbouj, M., Zhou, K., Elad, M., Xu, K. (eds) Image and Graphics. ICIG 2021. Lecture Notes in Computer Science(), vol 12889. Springer, Cham. https://doi.org/10.1007/978-3-030-87358-5_13
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