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title: "On a method for Rock Classification using Textural Features and Genetic Optimization • CBPF Index"
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**Technical Note****Published**

# **On a method for Rock Classification using Textural Features and Genetic Optimization**

**Manuel Blanco Valentín; Bom, C.R.; M. Portes de Albuquerque; Marcelo P. de Albuquerque; E. L. de Faria; Maury Duarte Correia; Rodrigo Surmas**

04/06/2017

[**Download PDF **1 MB](https://index.cbpf.br/files/publicacao/6ad2a767639f62b11adbcf18/pdf) [**Contact owner**](mailto:apub@cbpf.br?subject=On%20a%20method%20for%20Rock%20Classification%20using%20Textural%20Features%20and%20Genetic%20Optimization)

## **Publication details**

<dl>

<dt>Authors</dt>
<dd>Manuel Blanco Valentín; Bom, C.R.; M. Portes de Albuquerque; Marcelo P. de Albuquerque; E. L. de Faria; Maury Duarte Correia; Rodrigo Surmas</dd>

<dt>Type</dt>
<dd>Technical Note</dd>

<dt>Status</dt>
<dd>Published</dd>

<dt>Publication date</dt>
<dd>06/04/2017</dd>

<dt>Description</dt>
<dd>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.</dd>

<dt>Year</dt>
<dd>2017</dd>

<dt>Number</dt>
<dd>CBPF-NT-003/17</dd>

<dt>Linked to a thesis</dt>
<dd>No</dd>

<dt>Linked to a grant</dt>
<dd>No</dd>

</dl>

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