Imperfect pattern recognition using the fuzzy measure theory

Anas Dahabiah (1, 2) , John Puentes (1, 2) , Basel Solaiman (1, 2)

1 ITI - Département Image et Traitement Information
2 LaTIM - Laboratoire de Traitement de l'Information Medicale

This paper aims to provide a unified framework to deal with information imperfection and heterogeneity using possibility theory, in addition to information conflict and scarcity using Dempster-Shafer theory in order to classify imperfectly-described medical images. The proposed method is very robust and general. It can be applied without modification to any other database.

Mots clés: Pattern recognition; Possibility theory; Dempster-Shafer theory; Similarity measurement

Domaines: Traitement du signal et de l'image [eess.SP]

Anas Dahabiah, John Puentes, Basel Solaiman. Imperfect pattern recognition using the fuzzy measure theory. Intelligent Data Engineering and Automated Learning - IDEAL 2009 , 2009, 5788, pp.101 - 108. ⟨hal-02137001⟩