Research on methods to differentiate coal and gangue using image processing and a support vector machine

Abstract

This article proposes a method that can be used to improve the differentiation of coal and gangue via image processing and use of a support vector machine (SVM). Images of coal and gangue were converted to grayscale in this approach, the background was segmented, and the contrast was stretched. A basic eigenvalue was then determined based on the contrast between the grayscale mean and the gray-level co-occurrence matrix in each image. The biorthogonal wavelet was then used to expand coal and gangue images based on discrete wavelet transforms in two dimensions (2-D), while the supplementary eigenvalue is comprised of the mean variance of the wavelet coefficient at different scales. The eigenvalue of coal was then contrasted with each gangue eigenvalue, as well as the basic and the supplementary eigenvalue to construct a mathematical recognition model based on image processing and use of a SVM. At the same time, the penalty factor and kernel function coefficient of the mathematical model were optimized using K-fold cross validation. Experimental results indicate that the method proposed in this article can be used to recognize coal and gangue more effectively (at a rate up to 95.12%), compared to the conventional image processing recognition method.