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Combining classifiers through fuzzy cognitive maps in natural images

Combining classifiers through fuzzy cognitive maps in natural images

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  • Author(s): G. Pajares 1 ; M. Guijarro 2 ; P.J. Herrera 1 ; A. Ribeiro 3
    • View affiliations
    • Affiliations: 1: Departamento de Ingeniería del Software e Inteligencia Artificial, Facultad Informática, Universidad Complutense, Madrid, Spain
      2: Centro Superior de Estudios Felipe II, Ingeniería Técnica en Informática de Sistemas, Aranjuez, Madrid, Spain
      3: Instituto de Automática Industrial, Consejo Superior de Investigaciones Científicas, Madrid, Spain
  • Source: Volume 3, Issue 3, September 2009, p. 112 – 123
    DOI:  10.1049/iet-cvi.2008.0023 , Print ISSN 1751-9632, Online ISSN 1751-9640
© The Institution of Engineering and Technology
Published

A new automatic hybrid classifier for natural images by combining two base classifiers through the fuzzy cognitive maps (FCMs) approach is presented in this study. The base classifiers used are fuzzy clustering (FC) and the parametric Bayesian (BP) method. During the training phase, different partitions are established until a valid partition is found. Partitioning and validation are two automatic processes based on validation measurements. From a valid partition, the parameters of both classifiers are estimated. During the classification phase, FC provides for each pixel the supports (membership degrees) that determine which cluster the pixel belongs to. These supports are punished or rewarded based on the supports (probabilities) provided by BP. This is achieved through the FCM approach, which combines the different supports. The automatic strategy and the combined strategy under the FCM framework make up the main findings of this study. The analysis of the results shows that the performance of the proposed method is superior to other hybrid methods and more accurate than the single usage of existing base classifiers.

Inspec keywords: pattern clustering; Bayes methods; geophysical signal processing; image resolution; fuzzy set theory

Other keywords: fuzzy cognitive maps; fuzzy clustering; natural images; parametric Bayesian method; aerial images

Subjects: Other topics in statistics; Computer vision and image processing techniques; Combinatorial mathematics; Geography and cartography computing; Combinatorial mathematics; Optical, image and video signal processing; Other topics in statistics; Geophysical techniques and equipment

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