• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Computational Intelligence for Image Processing, 2009. CIIP '09 >Hybridization of particle swarm optimization with the K-Means algorithm for image classification
【24h】

Hybridization of particle swarm optimization with the K-Means algorithm for image classification

机译：粒子群算法与K-Means算法的混合用于图像分类

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The K-means algorithm is one of the widely used clustering algorithms in the image classification systems. However, the K-Means algorithm is easily trapped into the local optimal solutions. Several optimization techniques have been proposed to solve this problem such as genetic algorithms, simulated annealing and swarm intelligence. In this paper, we develop hybrid techniques using different particle swarm optimization (PSO) heuristics to optimize the k-means algorithm and examine the reliability of parametric values for different variants of PSO and k-means algorithms. These PSO heuristics include linear inertia reduction, constriction factor, and dynamic inertia and maximum velocity reduction. The performance of these hybridization of PSO and the k-means algorithms was tested on the image segmentation. These PSO heuristics can make the K-means algorithm more stable for finding better solutions and less dependent on the initial cluster centers based on the preliminary experimental results.
机译：K-均值算法是图像分类系统中广泛使用的聚类算法之一。但是，K-Means算法很容易陷入局部最优解中。已经提出了几种优化技术来解决该问题，例如遗传算法，模拟退火和群体智能。在本文中，我们使用不同的粒子群优化（PSO）启发式技术开发混合技术，以优化k-means算法，并检查PSO和k-means算法不同变体的参数值的可靠性。这些PSO启发式方法包括线性惯性减小，压缩因子以及动态惯性和最大速度减小。在图像分割上测试了PSO和k-means算法的这些杂交性能。这些PSO启发式方法可以使K-means算法更稳定，从而可以找到更好的解决方案，并且根据初步实验结果，可以减少对初始聚类中心的依赖。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号