Information fusion for unsupervised image segmentation using stochastic watershed and Hessian matrix

C. Chahine (1) , C. Lagorre , Y. Chenoune (1) , R. El-Berbari , Z. El Fawal , E. Petit (1)

1 SIMO

This study deals with information fusion for image segmentation. The evidence theory (or the Dempster–Shafer theory) allows the modellisation of uncertainty and imprecision in the information as well as the combination of different sources. Here, this approach is used in an unsupervised framework to combine the stochastic watershed segmentation which provides several segmentation results, with a Hessian operator in order to obtain a unique and efficient segmentation. The method is tested on natural images from the Berkeley dataset and evaluated using several evaluation metrics. The fusion results surpass those obtained with stochastic watershed alone.

Mots clés: Information fusion; Unsupervised image segmentation; Stochastic watershed; Evidence theory

Domaines: Informatique [cs]; Traitement des images [eess.IV]

C. Chahine, C. Lagorre, Y. Chenoune, R. El-Berbari, Z. El Fawal, et al.. Information fusion for unsupervised image segmentation using stochastic watershed and Hessian matrix. IET Image Processing, 2017, 12 (4), pp.525-531. ⟨hal-01845853⟩