Multiclass mineral recognition using similarity features and ensembles of pair-wise classifiers

Authors:
Rimantas Kybartas;Nurdan Akhan Baykan;Nihat Yilmaz;Sarunas Raudys
Affiliations:
Department of Computer Science, Vilnius University, Vilnius, Lithuania;Department of Computer Engineering, University of Selcuk, Konya, Turkey;Department of Electric-Electronics Engineering, University of Selcuk, Konya, Turkey;Department of Computer Science, Vilnius University, Vilnius, Lithuania
Venue:
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
Year:
2010

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Abstract

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 classifiers and two-stage classifiers based on simple pair-wise classification algorithms. Results show that employment of two-stage classifiers with proper parameters or K class single layer perceptron are good choices for mineral classification. Similarity features with properly selected parameters allow obtaining non-linear decision boundaries and lead to sizeable decrease in classification error rate.