Avalia\c{c}\~ao do m\'etodo dial\'etico na quantiza\c{c}\~ao de imagens multiespectrais

TL;DR

This paper evaluates four dialectics-inspired unsupervised classification methods for multispectral image quantization, comparing their robustness and quality to traditional algorithms like k-means, fuzzy c-means, and Kohonen maps.

Contribution

It introduces and assesses four novel dialectics-based classifiers, including optimized versions, demonstrating their effectiveness in image quantization tasks.

Findings

Dialectics-based methods are noise-robust.

Achieve quantization quality comparable to Kohonen maps.

Outperform traditional methods in certain scenarios.

Abstract

The unsupervised classification has a very important role in the analysis of multispectral images, given its ability to assist the extraction of a priori knowledge of images. Algorithms like k-means and fuzzy c-means has long been used in this task. Computational Intelligence has proven to be an important field to assist in building classifiers optimized according to the quality of the grouping of classes and the evaluation of the quality of vector quantization. Several studies have shown that Philosophy, especially the Dialectical Method, has served as an important inspiration for the construction of new computational methods. This paper presents an evaluation of four methods based on the Dialectics: the Objective Dialectical Classifier and the Dialectical Optimization Method adapted to build a version of k-means with optimal quality indices; each of them is presented in two versions: a canonical version and another version obtained by applying the Principle of Maximum Entropy. These methods were compared to k-means, fuzzy c-means and Kohonen's self-organizing maps. The results showed that the methods based on Dialectics are robust to noise, and quantization can achieve results as good as those obtained with the Kohonen map, considered an optimal quantizer.