Use of gray value distribution of run lengths for texture analysis
Pattern Recognition Letters
Image characterizations based on joint gray level-run length distributions
Pattern Recognition Letters
The nature of statistical learning theory
The nature of statistical learning theory
A comparison of genetic algorithms and other machine learning systems on a complex classification task from common disease research
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
An improved branch and bound algorithm for feature selection
Pattern Recognition Letters
ICIIS '99 Proceedings of the 1999 International Conference on Information Intelligence and Systems
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Fast Branch & Bound Algorithms for Optimal Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient huge-scale feature selection with speciated genetic algorithm
Pattern Recognition Letters
Automatic thresholding for defect detection
Pattern Recognition Letters
An improved watershed algorithm based on efficient computation of shortest paths
Pattern Recognition
Locally adaptable mathematical morphology using distance transformations
Pattern Recognition
Image-based quality monitoring system of limestone ore grades
Computers in Industry
Engineering Applications of Artificial Intelligence
Fundamentals of Digital Image Processing: A Practical Approach with Examples in Matlab
Fundamentals of Digital Image Processing: A Practical Approach with Examples in Matlab
Simultaneous optimization of artificial neural networks for financial forecasting
Applied Intelligence
Integrated feature architecture selection
IEEE Transactions on Neural Networks
A novel feature selection method based on normalized mutual information
Applied Intelligence
A multi-threshold segmentation approach based on Artificial Bee Colony optimization
Applied Intelligence
Image annotation by modeling Supporting Region Graph
Applied Intelligence
| Hi-index | 0.00 |
Rock-type classification is a challenging and difficult job due to the heterogeneous properties of rocks. In this paper, an image-based rock-type analysis and classification method is proposed. The study was conducted at a limestone mine in western India using stratified random sampling from a case study mine. The analysis of collected sample images was performed in laboratory. Color, morphology, and textural features were extracted from the captured image and a total of 189 features were recorded. The multi-class support vector machine (SVM) algorithm was then applied for rock-type classification. The hyper-parameters and the number of input features of the SVM model were selected by genetic algorithm. The results revealed that the SVM model performed best when 40 features were selected out of the 189 extracted features. The results demonstrated that the overall accuracy of the proposed technique for rock type classification is 96.2 %. A comparative study shows that the proposed SVM model performed better than a competing neural network model in this case study mine.