• search hit 1 of 1
Back to Result List

Supervised and Transductive Multi-Class Segmentation Using p-Laplacians and RKHS methods

  • This paper considers supervised multi-class image segmentation: from a labeled set of pixels in one image, we learn the segmentation and apply it to the rest of the image or to other similar images. We study approaches with p-Laplacians, (vector-valued) Reproducing Kernel Hilbert Spaces (RKHSs) and combinations of both. In all approaches we construct segment membership vectors. In the p-Laplacian model the segment membership vectors have to fulfill a certain probability simplex constraint. Interestingly, we could prove that this is not really a constraint in the case p=2 but is automatically fulfilled. While the 2-Laplacian model gives a good general segmentation, the case of the 1-Laplacian tends to neglect smaller segments. The RKHS approach has the benefit of fast computation. This direction is motivated by image colorization, where a given dab of color is extended to a nearby region of similar features or to another image. The connection between colorization and multi-class segmentation is explored in this paper with an application to medical image segmentation. We further consider an improvement using a combined method. Each model is carefully considered with numerical experiments for validation, followed by medical image segmentation at the end.

Download full text files

Export metadata

Additional Services

Share in Twitter Search Google Scholar
Author:Sung Ha Kang, Behrang Shafei, Gabriele Steidl
URN (permanent link):urn:nbn:de:hbz:386-kluedo-31695
Document Type:Preprint
Language of publication:English
Publication Date:2012/06/08
Year of Publication:2012
Publishing Institute:Technische Universität Kaiserslautern
Date of the Publication (Server):2012/06/08
Faculties / Organisational entities:Fachbereich Mathematik
DDC-Cassification:5 Naturwissenschaften und Mathematik / 510 Mathematik
Licence (German):Standard gemäß KLUEDO-Leitlinien vom 15.02.2012