On a method for Rock Classification using Textural Features and Genetic Optimization
     Sobre um método de classificação de rochas usando features de texturas e otimização genética

Manuel Blanco Valentín, Clécio Roque de Bom, Márcio P. de Albuquerque, Marcelo P. de Albuquerque, Elisângela L. Faria, Maury Correia, Rodrigo Surmas

Resumo


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 Na\"{i}ve 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 $\sim$ 70\% success ratio (without any optimization nor filters used). After the optimization $9$ types of features emerged as most relevant.

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DOI: http://dx.doi.org/10.7437/nt-cbpf.v7i1.242