Motion Segmentation Using Distributed Genetic Algorithms

Abstract

AbstractThis paper presents a Bayesian framework for simultaneous motion segmentation and estimation using genetic algorithms (GAs). The segmentation label and motion field are modeled by Markov random fields (MRFs), and a MAP estimate is used to identify the optimal label and motion field. In this paper, the motion segmentation and estimation problems are formalized as optimization problems of the energy function. And, the process for optimization of energy function is performed by iterating motion segmentation and estimation using a genetic algorithm, which is robust and effective to deal with combinatorial problems. The computation is distributed into chromosomes that evolve by distributed genetic algorithms (DGAs). Experimental results shows that our proposed method estimates an accurate motion field and segments a satisfactory label fields.KeywordsGenetic AlgorithmEnergy FunctionMotion VectorMotion EstimationMarkov Random FieldThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.