Classification of steel microstructures using Modified Alternate Local Ternary Pattern

Steel is the most preferred and highly demanded material on the Earth due to its recyclable properties. It has high durability and strength which makes it an important material for modern construction. Based on the carbon content, plain carbon steel is categorized into low, medium and high carbon steel. Usually, microstructural classification is done manually by the metallurgists. Automation can be brought in with the help of digital image processing techniques. In this article, a novel texture descriptor named Modified Alternate Local Ternary Pattern (MALTP) has been introduced for automatic classification of steel microstructures. The database consists of 300 optical microstructural images of three steel grades (low, medium and high carbon steel) under 100 X magnification. In the proposed method, the texture features extracted by MALTP are used for microstructural classification with two fold (k = 2) cross validation. Classification is performed using machine learning techniques such as K-Nearest Neighbors (K-NN) and Support Vector Machine (SVM). The performance of SVM in microstructural classification with respect to different kernels like linear, polynomial and radial basis function are analyzed. Out of these machine learning techniques, SVM with radial basis function kernel provides the highest accuracy of 98.89%.