Particle Swarm Optimization Algorithm And It's Application On Image Segmentation

Particle swarm optimization algorithm (PSO) is inspired by social behavior of bird flocking or fish schooling.It is a population-based, self-adaptive search optimization technique.As a kind of swarm intelligence, it has been proven to be an effective global optimization method. PSO algorithm has attracted a lot of attention from researchers around the world since it was put forward. It has already been successfully used in many areas, such as image segmentation, function optimization, artificial neural network training, and fuzzy system control.Image segmentation is regarded as an important step in object examination and recognition.The main goal is to separate objects of interest from an acquired image,so it provides the evidence to the subsequent processing of computer vision.Several methods are proposed from different theoretical point of view for image segmentation. Image threshold segmentation is an effective tool for image segmentation because the simple implemention. However, the problem of time-consuming computation will not meet real-time requirement when we try to search optimum multilevel thresholds on a multimodal histogram of a complex image. But exactly determining those thresholds is the key for effective image segmentation.So it is a difficult problem for us to quickly and exactly search optimum multilevel thresholds for image segmentation.However, to quickly and exactly determine optimum combination of multilevel thresholds, which can segment the image efficiently and meet real-time requirement, we must explore an effective and rapid algorithm to solve the problem of image segmentation based on multilevel thresholds.Based on the former research,the author studies the improvement of particle swarm algorithm and its application on image segmentation:Firstly, in order to improve the particle swarm algorithm convergence speed and also improve the global search function algorithm, this paper focuses on rearching two novel improved particle swarm algorithm. (a)The first kind of improved algorithm is adopted Opposition-based Learning initialization particle population, to gain more optimal initial solution. This Algorithm in order to further enhances the convergence speed and precision, when the group into the local optimal, produced the corresponding variation particles, compare their fitness, the selection of the best fitness particle continue to optimize process. According to the different test function of simulation experiment shows that the improved particle swarm algorithm is significantly improved tne algorithm convergence speed and precision. (b)The second kind of improved algorithm combins particle swarm algorithm with immune algorithm and using simulated annealing mechanism of particle position limit, and traveling salesman problem verifies the effectiveness of the combinatorial optimization algorithm.Secondly, the two improved algorithms are applied to image segmentation experiments, based on multi threshold value. The experiment showed that the two improved algorithms can rapidly and accurately find the best combination of thresholds, obtain good segmentation results and suitable for complex image with multi-modal histogram.