Image Segmentation by Quantum-Behaved Particle Swarm Optimization Algorithms
Sun Jun · Computer Engineering and Applications Journal · 2006
General purposed color image segmentation is a challenging and important issue in image processing.Computing an exact texture fields and the optimum number of segmentation areas in an image is difficult,when it contains similar and/or unstationary texture fields.In this paper,we are seeking for a practical and generic solution to image segmentation,that is Quantum-Behaved Particle Swarm Optimization Algorithms.We formulate the segmentation problem upon such images as an optimization problem and adopt evolutionary strategy of QPSO for the clustering of regions in color feature.Not only parameters of QPSO is few and randomicity of QPSO is strong,but also QPSO cover with all solution space and guarantee global convergence of algorithms.Three images results of segmentation are presented,and demonstrate the efficiency of QPSO algorithms to automatic and unsupervised color segmentation.