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快速自适应非局部空间加权与隶属度连接的模糊C-均值噪声图像分割算法

王小鹏 王庆圣 焦建军 梁金诚

王小鹏, 王庆圣, 焦建军, 梁金诚. 快速自适应非局部空间加权与隶属度连接的模糊C-均值噪声图像分割算法[J]. 电子与信息学报. doi: 10.11999/JEIT191016
引用本文: 王小鹏, 王庆圣, 焦建军, 梁金诚. 快速自适应非局部空间加权与隶属度连接的模糊C-均值噪声图像分割算法[J]. 电子与信息学报. doi: 10.11999/JEIT191016
Xiaopeng WANG, Qingsheng WANG, Jianjun JIAO, Jincheng LIANG. Fuzzy C-Means Clustering with Fast and Adaptive Non-local Spatial Constraint and Membership Linking for Noise Image Segmentation[J]. Journal of Electronics and Information Technology. doi: 10.11999/JEIT191016
Citation: Xiaopeng WANG, Qingsheng WANG, Jianjun JIAO, Jincheng LIANG. Fuzzy C-Means Clustering with Fast and Adaptive Non-local Spatial Constraint and Membership Linking for Noise Image Segmentation[J]. Journal of Electronics and Information Technology. doi: 10.11999/JEIT191016

快速自适应非局部空间加权与隶属度连接的模糊C-均值噪声图像分割算法

doi: 10.11999/JEIT191016
基金项目: 国家自然科学基金(61761027)
详细信息
    作者简介:

    王小鹏:男,1969年生,博士,教授,研究方向为图像分析,信号与信息处理

    王庆圣:男,1995年生,硕士生,研究方向为图像处理与模式识别

    焦建军:男,1984年生,博士生,研究方向为图像分析

    梁金诚:男,1997年生,硕士生,研究方向为图像处理与目标检测

    通讯作者:

    王小鹏 wangxp1969@sina.com

  • 中图分类号: TP391.4

Fuzzy C-Means Clustering with Fast and Adaptive Non-local Spatial Constraint and Membership Linking for Noise Image Segmentation

Funds: The National Natural Science Foundation of China (61761027)
  • 摘要: 针对传统模糊C-均值聚类(FCM)算法难以对噪声图像进行分割的问题,该文提出一种快速自适应非局部空间加权与隶属度连接的模糊FCM抗噪图像分割算法。首先,利用一种非局部空间信息快速计算方法,将以图像所有像素为循环的原始非局部信息计算方法,改为以搜索窗口尺寸为循环,利用空间位移图像与递归高斯滤波的计算方法,克服非局部空间信息计算复杂的问题;其次,计算原始图像与非局部信息项的差值的平方,将其作为非局部信息项的自适应权重,并将差值的平方作倒数变换,作为原始图像的自适应权重;最后,将每个聚类簇中所有像素隶属度之和的对数平方加入目标函数的分母,形成隶属度连接,减少目标函数迭代次数。含噪人工与自然图像分割实验表明,该算法在分割准确度、平均交并比、归一化互信息、运行时间与迭代次数等性能方面优于其他几种FCM算法。
  • 图  1  5种算法对含5%混合噪声人工图像的分割结果(K=4)

    图  2  5种算法对含20%混合噪声齿轮图像的分割结果(K=2)

    图  3  5种算法对含10%混合噪声#42049的分割结果(K=2)

    图  4  5种算法对含10%混合噪声#86016的分割效果(K=2)

    图  5  5种算法对含5%混合噪声#118035的分割效果(K=3)

    表  1  5种算法对含不同混合噪声人工图像的分割结果

    分割指标SA(%)mIoU(%)NMI(%)运行时间(s)迭代次数
    噪声大小(%)51015205101520510152051015205101520
    FCM55.0943.7731.2128.2152.6548.4835.4130.4125.3414.429.097.130.480.630.670.6918363235
    FLICM89.5470.8960.0052.0884.9564.2555.1649.5173.3250.1038.7130.907.527.947.238.02108125106130
    FCM-NLS96.8391.4876.2659.4396.3587.4068.4647.8085.9376.9753.6338.10185.57194.82195.57195.1424334747
    SNLS-IFCM97.6692.2377.0461.0296.6788.7570.1254.5986.0079.1857.8340.13184.12196.07196.40196.8317405173
    本文算法98.0297.3095.2690.1297.7095.5492.4086.7192.2389.8884.3574.841.851.922.092.0910101617
    下载: 导出CSV

    表  2  5种算法对含不同混合噪声齿轮图像的分割结果

    分割指标SA(%)mIoU(%)NMI(%)运行时间(s)迭代次数
    噪声大小(%)51015205101520510152051015205101520
    FCM90.5881.8274.5770.8882.6068.8858.8254.4154.9931.4217.8012.320.180.200.150.1815161618
    71.5663.1658.7455.8351.1642.7438.7436.2615.74 7.56 4.50 3.020.460.440.350.2223211611
    61.8757.5154.0451.5140.9636.5234.0332.04 8.19 3.27 2.22 1.330.370.580.280.3418191416
    55.8749.2346.7723.5036.5030.7328.1212.3519.45 9.96 6.11 4.111.020.760.520.7035251723
    FLICM98.8398.1196.6894.4197.6896.2793.5289.3690.8086.4478.9069.031.604.523.664.29647988109
    95.3393.1990.7688.6186.3180.9275.3970.9063.8152.8642.4634.6312.3211.4113.6916.14158148177212
    92.1992.8885.8284.0778.5978.1065.4561.0852.9146.0128.0219.9813.229.5823.6122.00157100300300
    62.1554.5344.4537.2046.3539.2030.8524.9949.0137.8127.2120.4718.8523.1718.4713.09132194151108
    FCM-NLS98.8498.1697.3895.8797.6896.3594.8491.9890.9886.9482.9076.62189.77190.08188.92197.1213161625
    95.3093.5383.5271.6286.2682.3965.3751.4963.6955.4231.1717.26472.53474.82468.91473.5129293630
    96.1384.8571.7063.3988.0065.1250.8842.7370.8537.3421.1511.77473.12470.12454.68472.6829283128
    85.8579.3172.2261.2567.4860.0852.3141.8462.8255.3644.6336.43478.75473.25479.75480.7535534949
    SNLS-IFCM98.9298.0597.3195.5097.8396.1494.7091.2991.4386.4082.5075.51188.42189.15188.50196.3011121318
    96.7593.8984.7272.6189.6482.8667.0152.5673.5756.6733.0318.48470.25465.61470.42472.7620254231
    96.8086.2171.9663.0189.7665.5151.2142.4979.8141.5521.9912.23470.30468.14452.14467.1423202824
    87.9279.3371.6363.1170.8059.8751.7243.8665.7854.1544.7235.16474.25471.81470.25472.2530392435
    本文算法99.2398.2697.4096.8998.1296.5595.1993.9092.0787.7083.5481.321.752.061.901.806668
    98.8994.3694.3493.4096.0484.5583.4582.1587.0161.0658.2755.884.534.414.784.447121413
    98.9797.7297.4691.8796.3192.3491.3878.1087.7978.3275.4453.154.535.514.534.37710711
    90.7187.4087.8279.0672.7566.2767.8560.0369.8465.5863.1354.464.864.374.814.9297917
    下载: 导出CSV

    表  3  5种算法的时间复杂度

    分割算法计算步骤$E$计算步骤函数$E(n)$时间复杂度
    FCM$H \times W \times K \times {\rm{iter} }$${n^4}$$O({n^4})$
    FLICM$H \times W \times K \times {k^2} \times {\rm{iter} }$${n^6}$$O({n^6})$
    FCM-NLS$H \times W \times {(2S + 1)^2} \times {(2s + 1)^2} + H \times W \times K \times {\rm{iter} }$${n^2}{(n + 1)^4} + {n^4}$$O({n^6})$
    SNLS-IFCM$H \times W \times {(2S + 1)^2} \times {(2s + 1)^2} + H \times W \times (K - 1) \times K \times {\rm{iter} }$${n^2}{(n + 1)^4} + {n^4}(n - 1)$$O({n^6})$
    本文算法$H \times W \times [{(2S + 1)^2} - 1] + (2 \times H \times W) \times K \times {\rm{iter} }$${n^2}[{(n + 1)^2} - 1] + 2{n^4}$$O({n^4})$
    下载: 导出CSV
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