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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均值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均值聚类的鲁棒彩色图像分割方法。选择颜色特征集的第一分量作为一维特征向量。为了降低计算复杂度,使用从像素空间到特征向量空间的映射来修改目标函数。应用适用于特征向量空间的任何结构的特征距离代替欧几里德距离,以克服特征向量空间的结构所造成的影响。使用彩色1D特征向量梯度二维直方图可自动获取簇数,以消除噪声。实验表明,该算法对彩色图像分割效果更好,计算复杂度较低。

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