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

A Backbone-Based Co-evolutionary Heuristic for Partial MAX-SAT

The concept of backbone variables in the satisfiability problem has been recently introduced as a problem structure property and shown to influence its complexity. This suggests that the performance of stochastic local search algorithms for satisfiability problems can be improved by using backbone information. The Partial MAX-SAT Problem (PMSAT) is a variant of MAX-SAT which consists of two CNF formulas defined over the same variable set. Its solution must satisfy all clauses of the first formula and as many clauses in the second formula as possible. This study is concerned with the PMSAT solution in setting a co-evolutionary stochastic local search algorithm guided by an estimated backbone variables of the problem. The effectiveness of our algorithm is examined by computational experiments. Reported results for a number of PMSAT instances suggest that this approach can outperform state-of-the-art PMSAT techniques

A Parallel Distributed System for Gene Expression Profiling Based on Clustering Ensemble and Distributed Optimization

With the development of microarray technology, it is possible now to study and measure the expression profiles of thousands of genes simultaneously which can lead to identify subgroup of specific disease or extract hidden relationships between genes. One computational method often used to this end is clustering. In this paper, we propose a parallel distributed system for gene expression profiling (PDS-GEF) which provides a useful basis for individualized treatment of a certain disease such as Cancer. The proposed approach is based on two major techniques: the GIM (Generalized Island Model) and clustering ensemble. GIMs are used to generate good quality clusterings which are refined by a consensus function to get a high quality clustering. PDS-GEF system is implemented using Matlab®’s PCT (Parallel Computing ToolboxTM) which runs on a desktop computer, and tested on 34 different publicly available gene expression data sets. The obtained results compete with and even outperform existing methods828

UNE APPROCHE BIOMIMETIQUE POUR LA SEGMENTATION D'IMAGES

Dans ce papier, nous proposons un système à base d’agents réactifs capable de segmenter des images en niveau de gris. Dans cette approche nous avons combiné le mécanisme de stigmergie qualitative observé chez les insectes sociaux avec un autre type de comportement d’essaim qui est l’Optimisation par l'Essaim de Particules (OEP). Les agents se déplacent sur l’image en construisant des régions homogènes ; par le regroupement des pixels connexes présentant une certaine similarité, sous une même marque. La formation d’une région commence à partir d’un pixel appelé pixel germe, puis sa croissance suit un processus itératif de regroupement des pixels voisins et connexes vérifiant un critère d’homogénéité. Le choix du pixel germe et l’estimation locale de l’homogénéité de la région sont traités par l’OEP, cependant, le contrôle du processus de construction de la région est réalisé par le mécanisme stigmergique. Les expérimentations ont été menées sur des images variées, pour montrer les caractéristiques de cette approche et présenter les résultats obtenus

RESOLUTION COLLECTIVE DU PROBLEME DE SEGMENTATION

L'un des buts de l'intelligence collective est la conception de systèmes artificiels adaptatifs, décentralisés, flexibles et robustesinspirés des phénomènes collectifs en biologie et qui soient capables de résoudre des problèmes. Plusieurs modèles inspirés desinsectes sociaux ont été élaborés et utilisés pour effectuer la résolution de problème. Chez les insectes sociaux, lecomportement collectif qui émerge des comportements simples des individus est nommé intelligence en essaim.Dans ce papier nous proposons une approche pour segmenter une image, en suivant le principe de l’incrémentalité ; les régionssont construites à partir du modèle incrémental de croissance de région, mais selon une direction de vie artificielle, encherchant à mettre en oeuvre une flexibilité plus grande. Nous allons adapter une des technologies clefs de la vie artificiellesqui est basée sur les techniques d'intelligence en essaim, et employer une grande population d’agents simples de faiblegranularité qui coordonnent leurs activités avec des interactions stigmergiques. En utilisant la phéromone artificielle, les agentss'organisent dynamiquement autour des régions homogènes. L’approche a été appliquée sur quelques images les résultats sontencourageants.MOTS CLES : Segmentation d’image, Vie artificielle, Emergence, Intelligence en essaim, Stigmergie, Phéromone, Agentsréactifs

Combining Fisher Discriminant Analysis and Probabilistic Neural Network for Effective On-line Signature Recognition

The advent of new technologies enables capturing the dynamic of a signature. This has opened a new perspective for the possible use of signatures as a basis for an authentication system that is accurate and trustworthy enough to be integrated in practical applications. Automatic online signature recognition and verification is one of the biometric techniques being the subject of a growing and intensive research activity. In this paper, we address this problem and we propose a two-stage approach for personal identification. The first stage consists in the use of linear discriminant analysis to reduce the dimensionality of the feature space while maintaining discrimination between user classes. The second stage consists in tailoring a probabilistic neural network for effective classification purposes. Several experiments have been conducted using SVC2004 database. Very high classification rates have been achieved showing the effectiveness of the proposed approach

Solving the Maximum Satisfiability Problem Using an Evolutionary Local Search Algorithm

Abstract: The MAXimum propositional SATisfiability problem (MAXSAT) is a well known NP-hard optimization problem with many theoretical and practical applications in artificial intelligence and mathematical logic. Heuristic local search algorithms are widely recognized as the most effective approaches used to solve them. However, their performance depends both on their complexity and their tuning parameters which are controlled experimentally and remain a difficult task. Extremal Optimization (EO) is one of the simplest heuristic methods with only one free parameter, which has proved competitive with the more elaborate general-purpose method on graph partitioning and coloring. It is inspired by the dynamics of physical systems with emergent complexity and their ability to self-organize to reach an optimal adaptation state. In this paper, we propose an extremal optimization procedure for MAXSAT and consider its effectiveness by computational experiments on a benchmark of random instances. Comparative tests showed that this procedure improves significantly previous results obtained on the same benchmark with other modern local search methods like WSAT, simulated annealing and Tabu Search (TS)

A Novel Approach for Online Signature Verification Using Fisher Based Probabilistic Neural Network

The rapid advancements in communication, networking and mobility have entailed an urgency to further develop basic biometric capabilities to face security challenges. Online signature authentication is increasingly gaining interest thanks to the advent of high quality signature devices. In this paper, we propose a new approach for automatic authentication using dynamic signature. The key features consist in using a powerful combination of linear discriminant analysis (LDA) and probabibilistic neural network (PNN) model together with an appropriate decision making process. LDA is used to reduce the dimensionality of the feature space while maintining discrimination between users. Based on its results, a PNN model is constructed and used for matching purposes. Then a decision making process relying on an appropriate decision rule is performed to accept or reject a claimed identity. Data sets from SVC 2004 have been used to assess the performance of the proposed system. The results show that the proposed method competes with and even outperforms existing methods