Paper Info
Title
Image Segmentation Using Mrf Model Optimized By A Hybrid Aco-Icm Algorithm
Abstract
Gray-scale image segmentation can be cast as a combinatorial optimization problem using a Markov Random Field (MRF) model. Local and global search algorithms were proposed to find an optimal solution in a reasonable amount of time. Both local and global search algorithms have their strengths and their shortcomings. The aim of this work is to propose and study a combination between local and global search strategies. For the local search we use the Iterated Conditional Modes (ICM) while the global optimization is performed by an Ant Colony System (ACS). ICM and ACS are both based on a MRF model to assess the local and the global quality of an arbitrary segmentation. The local optimization is performed by combining Maximum Likelihood (ML) for estimation of the parameters and ICM to find the solution given a fixed set of these parameters. A comparative study is performed to select the combination strategy that gives a better result. The effectiveness of our proposal is proven through experimentation. The problem of parameterization of our solution is addressed through an empirical study that shows the effect of each parameter on the behavior of the proposed solution.
Year
DOI
Venue
2021
10.1007/s00500-021-05957-1
SOFT COMPUTING
Keywords
DocType
Volume
Markov random fields, Maximum likelihood, Iterated conditional modes, Ant colony system, HyperCube framework
Journal
25
Issue
ISSN
Citations 
15
1432-7643
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Houcemeddine Filali100.34
Karim Kalti2208.58