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Communication Dans Un Congrès Année : 1999

Knowledge Based Supervised Classification: an Application to Image Processing

Amel Borgi
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Herman Akdag
  • Fonction : Auteur
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Résumé

We present a knowledge based supervised classification method. Our modelisation is based on automatic generation of classification rules. The classification function is directly given in the form of production rules base. The proposed learning method is multi-features, it allows to take into account the possible predictive power of a simultaneously considered features conjunction. On the other hand, the feature space partition allows a multi-valued representation of the features and data imprecision integration. The rules conclusions are accompanied by belief degrees. This uncertainty is managed in the learning phase as well as in the recognition one. To introduce more flexibility and overcome the boundary problem due to the discretisation, we propose to use approximate reasoning. We introduce, in this purpose, an adequate distance to compare neighboring facts. This distance, measuring imprecision, combined with uncertainty of classification decisions represented by belief degrees, drives the approximate inference. The proposed method was implemented in a tool called SUCRAGE and confronted with a real application in the field of image processing. The obtained results are very satisfactory. They validate our approach and allow us to consider other application fields.
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Dates et versions

hal-01573988 , version 1 (11-08-2017)

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  • HAL Id : hal-01573988 , version 1

Citer

Amel Borgi, Herman Akdag. Knowledge Based Supervised Classification: an Application to Image Processing. Eusflat-Estylf Conference, Sep 1999, Palma de Mallorca, Spain. pp.175-178. ⟨hal-01573988⟩
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