On a method for Rock Classification using Textural Features and Genetic Optimization
Abstract
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.
Cite
@article{arxiv.1607.01679,
title = {On a method for Rock Classification using Textural Features and Genetic Optimization},
author = {Manuel Blanco Valentin and Clecio Roque De Bom and Marcio Portes de Albuquerque and Marcelo Portes de Albuquerque and Elisangela Faria and Maury Duarte Correia and Rodrigo Surmas},
journal= {arXiv preprint arXiv:1607.01679},
year = {2017}
}
Comments
13 pages, 3 figures, 1 appendix. Replaced to match the published version