基于QPSO算法的图像颜色分割*

目前不同种类的纹理区域组成的彩色图像分割还是一个难点。当一幅图像中包含相似的和（或）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数目。描述了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。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纹理分割上具有很好的效果。

Image Color Segmentation Based on QPSO Algorithms

At present, Segmentation of a color image composed of different kinds of texture regions is still a difficulty. Computing the 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, describing image color segmentation by QPSO. 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 are few and randomicity of QPSO is strong, but also QPSO covers 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 texture segmentation.