On a method for Rock Classification using Textural Features and Genetic Optimization
Manuel Blanco Valentin, Clecio Roque De Bom, Marcio Portes de Albuquerque, Marcelo Portes de Albuquerque, Elisangela Faria, Maury Duarte Correia, Rodrigo Surmas
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In this work we present a method to classify a set of rock textures based on a Spectral Analysis and the extraction of the texture Features of the resulted images. Up to 520 features were tested using 4 different filters and all 31 different combinations were verified. The classification process relies on a Naive Bayes classifier. We performed two kinds of optimizations: statistical optimization with covariance-based Principal Component Analysis (PCA) and a genetic optimization, for 10,000 randomly defined samples, achieving a final maximum classification success of 91% against the original 70% success ratio (without any optimization nor filters used). After the optimization 9 types of features emerged as most relevant.