计算机应用 ›› 2010, Vol. 30 ›› Issue (12): 3258-3261.

• 多媒体与软件技术 • 上一篇    下一篇

粒子群算法优化归一化划分的彩色图像分割

翟艳鹏[Author]) AND 1[Journal]) AND year[Order])" target="_blank">翟艳鹏1,郭敏2,马苗1,贺姣3   

  1. 1. 陕西师范大学计算机科学学院
    2. 陕西师范大学 计算机科学学院
    3.
  • 收稿日期:2010-06-12 修回日期:2010-07-29 发布日期:2010-12-22 出版日期:2010-12-01
  • 通讯作者: 翟艳鹏
  • 基金资助:

    基于图形处理器的高性能计算;基于图形处理器的高性能计算;中央高校基本科研业务费专项资金资助

Color image segmentation of normalized cut and particle swarm optimization algorithm

  • Received:2010-06-12 Revised:2010-07-29 Online:2010-12-22 Published:2010-12-01

摘要:

为克服谱聚类算法求解归一化彩色图像划分时计算复杂度高、寻优能力差的不足,先对彩色图像各通道进行模糊C均值聚类,综合各通道聚类结果获得待分割图像,构造无向带权图;再使用二进制离散化粒子群算法替代谱聚类算法求解归一化划分准则的最小值,最后通过最优粒子获得分割结果。实验表明该方法耗时少,能完整准确地提取彩色图像中的目标。

关键词: 归一化划分, 粒子群优化算法, 模糊C均值聚类, 彩色图像分割

Abstract:

Spectral clustering minimizing normalized cut criterion has a high computational complexity and an inaccurate result in color image segmentation. In order to overcome these disadvantages, the paper firstly used Fuzzy C-Means (FCM) to deal with three channels of color image, obtained pre-segmentation image from these channels cluster results to construct undirected weighted graph; and then minimized normalized cut criterion using discrete particle swarm optimization algorithm instead of spectral clustering; finally, pre-segmentation result was obtained by the optimal particle. The experimental results show that the method is less time-consuming, and obtains a precise segmentation result in color image segmentation.

Key words: normalized cut, particle swarm optimization algorithm, Fuzzy C-Means (FCM), color image segmentation