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Texture segmentation based on an adaptively fuzzy clustering neural network

机译:基于自适应模糊聚类神经网络的纹理分割

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This work presents a novel approach to the segmentation of a textured image. We give a new validity function to check the validity of cluster number, it ensures the clustering results being fit for the real data structure by the aid of training of neural network. Then we synthesize traditional fuzzy clustering approaches and neural network to research the texture segmentation. The adaptive algorithm mainly includes three process: (1) feature extraction, extracting the texture features; (2) feature classification, using adaptively neural network to determine the clusters number; (3) fuzzy clustering, getting the results of classification and segmentation. Our experiments have proved the effectiveness of this method.
机译:这项工作提出了一种纹理图像分割的新方法。我们给出了一个新的有效功能来检查集群编号的有效性,它确保通过培训神经网络的培训来实现群集结果适合真实数据结构。然后,我们综合传统的模糊聚类方法和神经网络来研究纹理分割。自适应算法主要包括三个过程:(1)特征提取,提取纹理特征; (2)特征分类,使用自适应性神经网络来确定簇数; (3)模糊聚类,获取分类和分割结果。我们的实验证明了这种方法的有效性。

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