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

© The Institution of Engineering and Technology
Received 03/10/2017, Accepted 18/11/2017, Published 22/11/2017

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.

Inspec keywords: image segmentation; unsupervised learning; Hessian matrices

Other keywords: unsupervised image segmentation; Berkeley dataset; Hessian matrix; stochastic watershed; Information fusion

Subjects: Computer vision and image processing techniques; Algebra; Algebra; Optical, image and video signal processing; Knowledge engineering techniques

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