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基于近邻搜索花授粉优化的直觉模糊聚类图像分割

赵凤 孙文静 刘汉强 曾哲

赵凤, 孙文静, 刘汉强, 曾哲. 基于近邻搜索花授粉优化的直觉模糊聚类图像分割[J]. 电子与信息学报, 2020, 42(4): 1005-1012. doi: 10.11999/JEIT190428
引用本文: 赵凤, 孙文静, 刘汉强, 曾哲. 基于近邻搜索花授粉优化的直觉模糊聚类图像分割[J]. 电子与信息学报, 2020, 42(4): 1005-1012. doi: 10.11999/JEIT190428
Feng ZHAO, Wenjing SUN, Hanqiang LIU, Zhe ZENG. Intuitionistic Fuzzy Clustering Image Segmentation Based on Flower Pollination Optimization with Nearest Neighbor Searching[J]. Journal of Electronics & Information Technology, 2020, 42(4): 1005-1012. doi: 10.11999/JEIT190428
Citation: Feng ZHAO, Wenjing SUN, Hanqiang LIU, Zhe ZENG. Intuitionistic Fuzzy Clustering Image Segmentation Based on Flower Pollination Optimization with Nearest Neighbor Searching[J]. Journal of Electronics & Information Technology, 2020, 42(4): 1005-1012. doi: 10.11999/JEIT190428

基于近邻搜索花授粉优化的直觉模糊聚类图像分割

doi: 10.11999/JEIT190428
基金项目: 国家自然科学基金(61571361, 61671377, 61102095),西安邮电大学西邮新星团队基金(xyt2016-01)
详细信息
    作者简介:

    赵凤:女,1980年生,教授,研究方向为计算智能与图像处理

    孙文静:女,1995年生,硕士生,研究方向为图像处理

    刘汉强:男,1981年生,副教授,研究方向为模式识别与图像处理

    曾哲:男,1995年生,硕士生,研究方向为图像处理

    通讯作者:

    赵凤 fzhao.xupt@gmail.com

  • 中图分类号: TP391

Intuitionistic Fuzzy Clustering Image Segmentation Based on Flower Pollination Optimization with Nearest Neighbor Searching

Funds: The National Natural Science Foundation of China (61571361, 61671377, 61102095), The New Star Team Foundation of Xi’an University of Posts & Telecommunications (xyt2016-01)
  • 摘要:

    为克服传统模糊聚类算法应用于图像分割时,易受噪声影响,对聚类中心初始值敏感,易陷入局部最优,模糊信息处理能力不足等缺陷,该文提出基于近邻搜索花授粉优化的直觉模糊聚类图像分割算法。首先设计一种新颖的图像空间信息提取策略,进而构造融合图像空间信息的直觉模糊聚类目标函数,提高对于噪声的鲁棒性,提升算法处理图像中模糊信息的能力。为了优化上述目标函数,提出一种基于近邻学习搜索机制的花授粉算法,实现对于聚类中心的寻优,解决对于聚类中心初始值敏感,易陷入局部最优的问题。实验结果表明所提算法能在多种噪声图像上取得令人满意的分割效果。

  • 图  1  各空间信息抗噪性能对比

    图  2  算法聚类准确率各类型噪声下随α变化结果

    图  3  算法聚类准确率各类型噪声下随β变化结果

    图  4  #241004的高斯噪声图像分割结果

    图  5  #241004的椒盐噪声图像分割结果

    图  6  #241004的混合噪声图像分割结果

    表  1  各算法聚类准确率对比

    图像噪声水平IFCMFPA-FCMFLICMIIFCMNDFCM本文算法
    高斯0.75360.73760.93060.76460.92790.9284
    #113016椒盐0.82540.83200.90190.82680.91190.9290
    高斯&椒盐0.78060.74430.91630.78060.90540.9175
    高斯0.83730.83570.90540.82340.89450.8986
    #101027椒盐0.79620.79390.85860.80410.88570.8913
    高斯&椒盐0.78060.78090.88340.77820.88390.8964
    高斯0.56400.56690.91010.56400.91120.8979
    #241004椒盐0.67250.67250.64620.67250.86620.9116
    高斯&椒盐0.53830.48470.64870.54420.84080.9012
    高斯0.83460.78880.93290.85700.93230.9332
    #15088椒盐0.84160.83950.93210.84210.93060.9331
    高斯&椒盐0.82250.79890.93260.82630.92850.9329
    高斯0.77190.83290.88830.63600.88060.8962
    #296059椒盐0.75000.4822083190.66710.86540.9022
    高斯&椒盐0.69750.27140.85300.60780.85820.8938
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-06-11
  • 修回日期:  2019-12-09
  • 网络出版日期:  2019-12-20
  • 刊出日期:  2020-06-04

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