Human expert fusion for image classification

Arnaud Martin (1) , Christophe Osswald (1)

1 E3I2 - Extraction et Exploitation de l'Information en Environnements Incertains

In image classification, merging the opinion of several human experts is very important for different tasks such as the evaluation or the training. Indeed, the ground truth is rarely known before the scene imaging. We propose here different models in order to fuse the informations given by two or more experts. The considered unit for the classification, a small tile of the image, can contain one or more kind of the considered classes given by the experts. A second problem that we have to take into account, is the amount of certainty of the expert has for each pixel of the tile. In order to solve these problems we define five models in the context of the Dempster-Shafer Theory and in the context of the Dezert-Smarandache Theory and we study the possible decisions with these models.

Mots clés: Experts fusion; DST; DSmT; image classification

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]; Intelligence artificielle [cs.AI]; Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Arnaud Martin, Christophe Osswald. Human expert fusion for image classification. INFORMATION & SECURITY. An International Journal, 2006, 20, pp.122-141. ⟨hal-00286589⟩