Multi-labeled image classification by TBM active learning

Hervé Goëau (1) , Michèle Rombaut (2) , Denis Pellerin (2) , Olivier Buisson (3)

1 INA - Institut National de l'Audiovisuel
2 GIPSA-AGPIG - GIPSA - Architecture, Géométrie, Perception, Images, Gestes
3 INA-SUP

Abstract--The Transferable Belief Model (TBM) is well adapted for knowledge representation, especially for complex systems. In this contribution, the TBM is used as a basic tool of an assistance system for image collection classification. The first part of the system, which is completely automatic, models all available knowledge provided by the already labeled images in order to structure the unlabeled ones. The second part is a user assistance system that proposes an ordered list of images to be labeled according to a specific strategy as well as a possible label. Via a suitable interface, the user agrees or not with the proposal and the global knowledge is updated.

Mots clés: image processing; active learning; Classification

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

Hervé Goëau, Michèle Rombaut, Denis Pellerin, Olivier Buisson. Multi-labeled image classification by TBM active learning. Belief 2010 - Workshop on Theory of Belief Functions, Apr 2010, Brest, France. pp.n.c. ⟨hal-00460886⟩