Tree-based iterated local search for Markov random fields with applications in image analysis

Tree-based iterated local search for Markov random fields with applications in image analysis

The maximum a posteriori (MAP) assignment for general structure Markov random fields (MRFs) is computationally intractable. In this paper, we exploit tree-based methods to efficiently address this problem. Our novel method, named Tree-based Iterated Local Search (T-ILS) takes advantage of the tractability of tree-structures embedded within MRFs to derive strong local search in an ILS framework....

Image registration with iterated local search

This contribution is devoted to the application of iterated local search to image registration, a very complex, real-world problem in the field of image processing. To do so, we first re-define this parameter estimation problem as a combinatorial optimization problem, then analyze the use of image-specific information to guide the search in the form of an heuristic function, and finally propose...

Image Segmentation Using Binary Tree Structured Markov Random Fields

Controller Placement in Software Defined Network using Iterated Local Search

Software defined network is a new computer network architecture who separates controller and data layer in network devices such as switches and routers. By the emerge of software defined networks, a class of location problems, called controller placement problem, has attracted much more research attention. The task in the problem is to simultaneously find optimal number and location of controll...

Pixon-based image segmentation with Markov random fields

Image segmentation is an essential processing step for many image analysis applications. We propose a novel pixon-based adaptive scale method for image segmentation. The key idea of our approach is that a pixon-based image model is combined with a Markov random field (MRF) model under a Bayesian framework. We introduce a new pixon scheme that is more suitable for image segmentation than the "fu...