A novel marble recognition system using extreme learning machine with LBP and histogram features

Marble classification in production facilities is a sensitive application, which results in light of the subjective decisions of experts. The expert classifies marble manually with its color, homogeneity, and texture in the process. An intelligent marble classifier based on image processing can provide solutions to current problems of the industry. In the proposed study, we introduce an intelligent classifier for marble classification with different classes in real field production. The purpose of the proposed intelligent model for marble facilities is to automate and enhance the manual classification process at present. The real-world dataset consists of Rosso-Levanto, Onyx, Keivan, and Black marble images. Local Binary Patterns and Histogram are used for feature extraction and Extreme Learning Machine is designed as an intelligent classifier. Decision Tree, Support Vector Machine, and Artificial Neural Network structures are also used for thorough performance analysis. The findings (successful test rate of 97.5%) reveal a high performance comparing to existing studies.