On a method for Rock Classification using Textural Features and Genetic Optimization
This work addresses rock classification for geology or related fields, but it is incremental as it combines existing techniques like PCA and genetic algorithms with standard classifiers.
The paper tackled rock texture classification by extracting up to 520 features from spectral analysis and using filters, with a Naive Bayes classifier achieving a 70% success rate initially. After applying PCA and genetic optimization on 10,000 samples, the method improved the classification success to 91%.
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.