A modified quantum-behaved particle swarm optimization algorithm for image segmentation

Summary from only given. One of the image segmentation methods are multilevel thresholding. There are many time-consuming algorithms. In this paper, we introduce particle swarm optimization (PSO) and employs the cooperative — quantumbehaved PSO (CQPSO) and then proposed modified cooperative method (CGQPSO) that base on Gaussian quantum-behaved particle swarm (GQPSO). The method which is proposed in this paper, can reach the best position faster than CQPSO. We use Optimum Global Thresholding using Otsu´s Method by calculating between-class variance as fitness function. The experimental results show that, the proposed algorithm (CGQPSO) gets results more stable than CQPSO algorithm in the small number of population and algorithm iteration. Moreover CGQPSO have computation time less than CQPSO so we can implement this algorithm to object recognition on the moving target.