Title: Particle Swarm Optimization Basedon Rotate Surface Transformation for Estimating Modal Parameter of Markov Random Field of Texture Image
Abstract: Particle Swarm Optimization(PSO) algorithm is a population-based global optimization algorithm,but it is easy to be trapped into local minima in optimizing multimodal function.Rotate Surface Transformation(RST) method was proposed to overcome the defect.RST method transforms local minima to global maximum and keeps the values of the function to be optimized unchanged where the value is lower than local minima.Markov random field(MRF) is a common model discribing texture image.It is critical question about how to estimate the parameter of MRF model in practical application.In this paper,the parameter is estimated through maximum pseudolikelihood method,RST technique is combined with PSO to overcome local minima question.Finally,some experiments was conducted with synthetic texture image.The experiment result proved that this method was efficient and effective.
Publication Year: 2005
Publication Date: 2005-01-01
Language: en
Type: article
Access and Citation
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot