Authors:
Thorsten Wilhelm
and
Christian Wöhler
Affiliation:
TU Dortmund, Germany
Keyword(s):
Bayesian, MCMC, Mixture Models, Segmentation, Superpixel, Texture.
Related
Ontology
Subjects/Areas/Topics:
Color and Texture Analyses
;
Computer Vision, Visualization and Computer Graphics
;
Image and Video Analysis
;
Segmentation and Grouping
Abstract:
The large computational demand is one huge drawback of Bayesian Mixture Models in image segmentation
tasks. We describe a novel approach to reduce the computational demand in this scenario and increase the performance
by using superpixels. Superpixels provide a natural approach to the reduction of the computational
complexity and to build a texture model in the image domain. Instead of relying on a Gaussian mixture model
as segmentation model, we propose to use a more robust model: a mixture of multiple scaled t-distributions.
The parameters of the novel mixture model are estimated with Markov chain Monte Carlo in order to surpass
local minima during estimation and to gain insight into the uncertainty of the resulting segmentation. Finally,
an evaluation of the proposed segmentation is performed on the publicly available Berkeley Segmentation
database (BSD500), compared to competing methods, and the benefit of including texture is emphasised.