From Feature Extraction to Classification: A multidisciplinary Approach applied to Portuguese Granites

TL;DR

This paper introduces a comprehensive multidisciplinary methodology for classifying Portuguese granites, combining mathematical morphology for feature extraction with genetic algorithms for feature selection, resulting in improved recognition accuracy.

Contribution

It presents a novel integrated approach that combines feature extraction, reduction, and classification using genetic algorithms and nearest neighbor classifiers for granite analysis.

Findings

Effective feature reduction improves recognition rate.

Methodology is robust for understanding image discriminant features.

Genetic algorithms enhance classifier performance.

Abstract

The purpose of this paper is to present a complete methodology based on a multidisciplinary approach, that goes from the extraction of features till the classification of a set of different portuguese granites. The set of tools to extract the features that characterise polished surfaces of the granites is mainly based on mathematical morphology. The classification methodology is based on a genetic algorithm capable of search the input feature space used by the nearest neighbour rule classifier. Results show that is adequate to perform feature reduction and simultaneous improve the recognition rate. Moreover, the present methodology represents a robust strategy to understand the proper nature of the images treated, and their discriminant features. KEYWORDS: Portuguese grey granites, feature extraction, mathematical morphology, feature reduction, genetic algorithms, nearest neighbour rule classifiers (k-NNR).