Fuzzy-Based Dialectical Non-Supervised Image Classification and Clustering

TL;DR

This paper introduces the Objective Dialectical Classifier (ODC), a novel non-supervised image classification method inspired by dialectical philosophy and fuzzy logic, demonstrating competitive performance on synthetic brain MRI images.

Contribution

The paper presents the ODC, a new dialectics-based extension of fuzzy c-means for non-supervised image classification, bridging philosophical concepts with computational methods.

Findings

ODC achieves quantization performance comparable to Kohonen's self-organized maps.

ODC effectively classifies synthetic brain MRI images.

The method integrates dialectical philosophy with fuzzy logic for image analysis.

Abstract

The materialist dialectical method is a philosophical investigative method to analyze aspects of reality. These aspects are viewed as complex processes composed by basic units named poles, which interact with each other. Dialectics has experienced considerable progress in the 19th century, with Hegel's dialectics and, in the 20th century, with the works of Marx, Engels, and Gramsci, in Philosophy and Economics. The movement of poles through their contradictions is viewed as a dynamic process with intertwined phases of evolution and revolutionary crisis. In order to build a computational process based on dialectics, the interaction between poles can be modeled using fuzzy membership functions. Based on this assumption, we introduce the Objective Dialectical Classifier (ODC), a non-supervised map for classification based on materialist dialectics and designed as an extension of fuzzy c-means classifier. As a case study, we used ODC to classify 181 magnetic resonance synthetic multispectral images composed by proton density, $T_1$- and $T_2$-weighted synthetic brain images. Comparing ODC to k-means, fuzzy c-means, and Kohonen's self-organized maps, concerning with image fidelity indexes as estimatives of quantization distortion, we proved that ODC can reach almost the same quantization performance as optimal non-supervised classifiers like Kohonen's self-organized maps.