Generic discounting evaluation approach for urban image classification

Ahmed Samet (1) , Imen Hammami (2) , Eric Lefevre (3) , Atef Hamouda (4)

1 ICube - Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie
2 MAP5 - UMR 8145 - Mathématiques Appliquées Paris 5
3 LGI2A - Laboratoire de Génie Informatique et d'Automatique de l'Artois
4 UTM - Université de Tunis El Manar

Belief function theory provides a robust framework for uncertain information modeling. It also offers several fusion tools in order to profit from multi-source context. Nevertheless, fusion is a sensible task where conflictual information may appear especially when sources are unreliable. Therefore, measuring source's reliability has been the center of many research and development. Existing solutions for source's reliability estimation are based on the assumption that distance is the only factor for conflictual situations. Indeed, integrating only distance measures to estimate source's reliability is not sufficient where source's confusion may be also considered as conflict origin. In this paper, we tackle reliability estimation and we introduce a new discounting operator that considers those two possible conflict origins. We propose an automatic method for discounting factor calculation. Those factors are integrated on belief classifier and tested on high-resolution image classification problem.

Mots clés: Belief function theory; Discounting; Classification; Conflict management; Source confusion

Domaines: Intelligence artificielle [cs.AI]

Ahmed Samet, Imen Hammami, Eric Lefevre, Atef Hamouda. Generic discounting evaluation approach for urban image classification. International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, IUKM'2013, Jul 2013, Beijing, China. pp.79-90, ⟨10.1007/978-3-642-39515-4_7⟩. ⟨hal-03649720⟩