Title
Bayesian inference for layer representation with mixed Markov random field
Abstract
This paper presents a Bayesian inference algorithm for image layer representation [26], 2.1D sketch [6], with mixed Markov random field. 2.1D sketch is an very important problem in low-middle level vision with a synthesis of two goals: segmentation and 2.5D sketch, in other words, it is to consider 2D segmentation by incorporating occulision/ depth explicitly to get the partial order of final segmented regions and contour completion in the same layer. The inference is based on Swendsen-Wang Cut (SWC) algorithm [4] where there are two types of nodes, instead of all nodes being the same type in traditional MRF model, in the graph representation: atomic regions and their open bonds desribed by address variables. These makes the problem a mixed random field. Therefore, two kinds of energies should be simultaneously minimized by maximizing a joint posterior probability: one is for region coloring/layering, the other is for the assignments of address variables. Given an image, its primal sketch is computed firstly, then some atomic regions can be obtained by completing some sketches into a closed contour. At the same time, T-junctions are detected and broken into terminators as the open bonds of atomic regions after being assigned the ownership between them and atomic regions. With this graph representation, the presented inference algorithm is performed and satisfactory results are shown in the experiments.
Year
Venue
Keywords
2007
EMMCVPR
image layer representation,open bond,graph representation,mixed markov random field,inference algorithm,address variable,atomic region,contour completion,closed contour,primal sketch,bayesian inference algorithm,partial order,bayesian inference,posterior probability,mcmc
Field
DocType
Volume
Mathematical optimization,Random field,Bayesian inference,Pattern recognition,Markov chain Monte Carlo,Inference,Computer science,Markov random field,Posterior probability,Artificial intelligence,Graph (abstract data type),Sketch
Conference
4679
ISSN
ISBN
Citations 
0302-9743
3-540-74195-X
12
PageRank 
References 
Authors
1.42
19
4
Name
Order
Citations
PageRank
Ruxin Gao1121.76
Tianfu Wu233126.72
Song-Chun Zhu36580741.75
Nong Sang447572.22