Dialectical non-supervised image classification

The materialist dialectical method is a philosophical investigative method to analyze aspects of reality as complex processes composed by integrating units named poles. 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. Santos et al. introduced the objective dialectical classifier (ODC), a non-supervised self-organized map for classification. As a case study, we used ODC to classify 181 magnetic resonance synthetic multispectral images composed by proton density, T₁- and T₂-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 the same quantization performance as optimal non-supervised classifiers like Kohonen´s self-organized maps.