Dialectical non-supervised image classification

• Published in: 2009 IEEE Congress on Evolutionary Computation pp. 2480 - 2487
• Main Authors: dos Santos, W.P., de Assis, F.M., de Souza, R.E., Mendes, P.B., Monteiro, H.S.S., Alves, H.D.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-05-2009
• Subjects: Biological neural networks; Brain modeling; Computational intelligence; Computer networks; Image classification; Magnetic resonance; Multispectral imaging; Pattern recognition; Protons; Quantization
• ISBN: 1424429587; 9781424429585
• ISSN: 1089-778X; 1941-0026
• DOI: 10.1109/CEC.2009.4983252

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, 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 the same quantization performance as optimal non-supervised classifiers like Kohonen's self-organized maps.