• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International Journal of Innovative Computing Information and Control >AN ADAPTIVE ACO-BASED FUZZY CLUSTERING ALGORITHM FOR NOISY IMAGE SEGMENTATION
【24h】

AN ADAPTIVE ACO-BASED FUZZY CLUSTERING ALGORITHM FOR NOISY IMAGE SEGMENTATION

机译：自适应ACO模糊聚类算法在噪声图像分割中的应用。

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The fuzzy c-means (FCM) has been a well-known algorithm in machine . learning/data mining area as a clustering algorithm. It can also be used for image segmentation, but the algorithm is not robust to noise. The possibilistic c-means (PCM) algorithm was proposed to overcome such a problem. However, the performance of PCM is too sensitive to the initialization of cluster centers, and often deteriorates due to the coincident clustering problem. To remedy these problems, we propose a new hybrid clustering algorithm that incorporates ACO (ant colony optimization)-based clustering into PCM, namely ACOPCM for noisy image segmentation. Our ACOPCM solves the coincident clustering problem by using pre-classified pixel information and provides the near optimal initialization of the number of clusters and their centroids. Quantitative and qualitative comparisons are performed on several images having different noise levels and bias-fields. Experimental results demonstrate that our proposed approach achieves higher segmentation accuracy than PCM and other hybrid fuzzy clustering approaches.
机译：模糊c均值（FCM）是机器中众所周知的算法。学习/数据挖掘领域作为聚类算法。它也可以用于图像分割，但是该算法对噪声不稳健。提出了可能的c均值（PCM）算法来克服这一问题。但是，PCM的性能对群集中心的初始化过于敏感，并且经常由于同时发生的群集问题而恶化。为了解决这些问题，我们提出了一种新的混合聚类算法，该算法将基于ACO（蚁群优化）的聚类纳入PCM，即用于噪声图像分割的ACOPCM。我们的ACOPCM通过使用预分类的像素信息解决了重合的聚类问题，并为聚类及其质心的数量提供了近乎最佳的初始化。对具有不同噪声水平和偏置场的几幅图像进行定量和定性比较。实验结果表明，我们提出的方法比PCM和其他混合模糊聚类方法具有更高的分割精度。

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