MRF-based image segmentation using Ant Colony System

In this paper, we propose a novel method for image segmentation that we call ACS-MRF method. ACS-MRF is a hybrid ant colony system coupled with a local search. We show how a colony of cooperating ants are able to estimate the labels field and minimize the MAP estimate. Cooperation between ants is performed by exchanging information through pheromone updating. The obtained results show the efficiency of the new algorithm, which is able to compete with other stochastic optimization methods like Simulated annealing and Genetic algorithm in terms of solution quality

MRF-based image segmentation using Ant Colony System

In this paper, we propose a novel method for image segmentation that we call ACS-MRF method. ACS-MRF is a hybrid ant colony system coupled with a local search. We show how a colony of cooperating ants are able to estimate the labels field and minimize the MAP estimate. Cooperation between ants is performed by exchanging information through pheromone updating. The obtained results show the efficiency of the new algorithm, which is able to compete with other stochastic optimization methods like Simulated annealing and Genetic algorithm in terms of solution quality

Ant colony system with local search for Markov random field image segmentation

ABSTRACT In this paper, we propose a new algorithm for image segtnentation based on the Markov Random Field (MRF) and the Ant Colony Optimization (ACO) metaheuristic. The underlying idea is to take advantage from the ACO nietaheuristic characteristics and the MRF theory to develop a novel ngents-based approach to segment an image. The proposed algorithm is based on a population of simple agents which construct a candidate partifion bv a relaxation labeling with respect to the contextual constraints. The obtained results show the efficiency of the new algorithm and that it competes with otlier global stoclinstic opfiinization nlethods like Simulated annealing and Genetic algorithm

PathME: pathway based multi-modal sparse autoencoders for clustering of patient-level multi-omics data

Recent years have witnessed an increasing interest in multi-omics data, because these data allow for better understanding complex diseases such as cancer on a molecular system level. In addition, multi-omics data increase the chance to robustly identify molecular patient sub-groups and hence open the door towards a better personalized treatment of diseases. Several methods have been proposed for unsupervised clustering of multi-omics data. However, a number of challenges remain, such as the magnitude of features and the large difference in dimensionality across different omics data sources.214