1 research outputs found
Unsupervised image segmentation by Global and local Criteria Optimization Based on Bayesian Networks
Today Bayesian networks are more used in many areas of decision support and
image processing. In this way, our proposed approach uses Bayesian Network to
modelize the segmented image quality. This quality is calculated on a set of
attributes that represent local evaluation measures. The idea is to have these
local levels chosen in a way to be intersected into them to keep the overall
appearance of segmentation. The approach operates in two phases: the first
phase is to make an over-segmentation which gives superpixels card. In the
second phase, we model the superpixels by a Bayesian Network. To find the
segmented image with the best overall quality we used two approximate inference
methods, the first using ICM algorithm which is widely used in Markov Models
and a second is a recursive method called algorithm of model decomposition
based on max-product algorithm which is very popular in the recent works of
image segmentation. For our model, we have shown that the composition of these
two algorithms leads to good segmentation performance.Comment: appears in International journal of robotics and imaging; volume 15,
issue 1, januray 201