Multiclass Mineral Recognition Using Similarity Features and Ensembles of Pair-Wise Classifiers

Mineral determination is a basis of the petrography Automatic mineral classification based on digital image, analysis is getting very popular To improve classification accuracy we consider similarity features. complex one stage classfiers and two-stage classifiers based on simple pair-wise classification algorithms Results show that employment of two-stage classifieis with proper parameters or K class single layer perceptron are good choices for mineral classification Similarity features with properly selected parameters allow obtaining non-lineal decision boundaries and lead to sizeable decrease in classification error rate