TY - INPR
A1 - Kang, Sung Ha
A1 - Shafei, Behrang
A1 - Steidl, Gabriele
T1 - Supervised and Transductive Multi-Class Segmentation Using p-Laplacians and RKHS methods
N2 - 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.
Y1 - 2012
UR - https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/3169
UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:386-kluedo-31695
ER -