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Texture image segmentation algorithm based on Nonsubsampled Contourlet Transform and SVM

机译:基于非下采样Contourlet变换和SVM的纹理图像分割算法

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In this paper we propose a novel texture image segmentation algorithm base on Nonsubsampled Contourlet Transform and SVM. Texture feature of image is extracted through decomposing image which uses the characteristic of multi-scale and multi-directional of Nonsubsampled Contourlet transform, and then, classifying the Feature Image by K neighbor classification algorithm and training support vector machine. Finally, segment the whole feature image by means of support vector machine. Three synthetic textures image segmentation experiment and comparison with other segmentation method results show that the correct rate of the proposed method of texture image segmentation is over 98%, and the method can be good for texture segmentation.
机译:本文提出了一种基于非下采样轮廓波变换和支持向量机的纹理图像分割算法。通过利用非下采样Contourlet变换的多尺度,多方向特征分解图像,提取图像的纹理特征,然后利用K邻居分类算法和训练支持向量机对特征图像进行分类。最后,利用支持向量机对整个特征图像进行分割。三种合成的纹理图像分割实验以及与其他分割方法结果的比较表明,所提出的纹理图像分割方法的正确率在98%以上,该方法可以很好地进行纹理分割。

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