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Robust color image segmentation method based on weighting Fuzzy C-Means Clustering

机译:基于加权模糊C型聚类的鲁棒彩色图像分割方法

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A robust color image segmentation method based on weighting Fuzzy C-Means Clustering (RWFCM) is proposed for color image segmentation. The first component of color feature set is chosen as the one-dimensional eigenvector. In order to reduce the computational complexity, the mapping from pixel space to eigenvector space is used for modifying the object function. Feature distance which is applied to any structure of eigenvector space is applied instead of Euclidian distance to overcome the influence caused by structure of eigenvector space. Using a Color 1D Eigenvector-Gradient two-dimensional histogram automatically get the number of clusters, In order to remove the noise. Experiments show that the algorithm has better effect and lower computational complexity on color image segmentation.
机译:提出了一种基于加权模糊C型聚类(RWFCM)的鲁棒彩色图像分割方法,用于彩色图像分割。彩色功能集的第一个组件被选为一维特征向量。为了降低计算复杂性,将从像素空间映射到特征向量空间用于修改对象功能。应用于以eGenvector空间的任何结构应用的特征距离而不是欧几里德距离来克服由特征传感器空间的结构造成的影响。使用颜色1D特征向量 - 梯度二维直方图自动获取群集数量,以便去除噪声。实验表明,该算法具有更好的效果和较低的彩色图像分割计算复杂性。

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