Dialectical non-supervised image classification

  • Authors:

  • Wellington P. Dos Santos;Francisco M. De Assis;Ricardo E. De Souza;Priscilla B. Mendes;Henrique S. S. Monteiro;Havana D. Alves

  • Affiliations:

  • Departamento de Engenharia Elétrica, Universidade Federal de Campina Grande, Campina Grande, PB, Brazil and Departamento de Sistemas e Computação, Universidade de Pernambuco, Recife ...;Departamento de Engenharia Elétrica, Universidade Federal de Campina Grande, Campina Grande, PB, Brazil;Departamento de Fisica, Universidade Federal de Pernambuco, Recife, PE, Brazil;Departamento de Sistemas e Computação, Universidade de Pernambuco, Recife, PE, Brazil;Departamento de Sistemas e Computação, Universidade de Pernambuco, Recife, PE, Brazil;Departamento de Sistemas e Computação, Universidade de Pernambuco, Recife, PE, Brazil

  • Venue:
  • CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
  • Year:
  • 2009

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Abstract

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.