基于量子行为的微粒群优化算法的图像分割

Image Segmentation by Quantum-Behaved Particle Swarm Optimization Algorithms

在图像处理中提出的图像颜色分割是一个重要性和具有挑战性的难题。当一幅图像中包含相似的和(或者)非固定的纹理区域时,难以计算出精确的纹理区域和分割区域的最优的数目。在这篇文章中,寻找出了一种实用而广泛的图像分割方法——基于量子行为的微粒群优化算法(QPSO)的图像颜色分割方法,把图像分割问题看作一个最优化问题,并且采用QPSO的进化策略聚类颜色特征空间中的区域。QPSO不仅参数个数少,随机性强,并且能覆盖所有解空间,保证算法的全局收敛。文中给出了三幅图像的分割效果,证明了QPSO算法在自动的和无监督的颜色分割上具有很好的效能。

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 diffficult,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.