2015/01/22 by Mohamed Ali Mahjoub, Mahjoub, Mohamed Ali, Mohamed Mhiri +1
Computer Science · #Artificial intelligence #Bayesian Modeling and Causal Inference #Bayesian inference #Bayesian network #Bayesian probability #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Data mining #FOS: Computer and information sciences #Image (mathematics) #Image segmentation #Inference #Machine Learning and Data Classification #Machine learning #Medical Image Segmentation Techniques #Pattern recognition (psychology) #Scale-space segmentation #Segmentation #Segmentation-based object categorization #Set (abstract data type) #cs.CV
paper · pdf · doi:10.48550/arxiv.1501.05617
published in arXiv (Cornell University) (Cornell University) · appears in International journal of robotics and imaging; volume 15, issue 1, januray 2015
arxiv created 2015/01/22 · openalex publication_date 2015/01/22 · arxiv updated 2015/01/23 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
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.