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Image segmentation based on local spectral histograms and linear regression

机译:基于局部光谱直方图和线性回归的图像分割

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We present a novel method for segmenting images with texture and nontexture regions. Local spectral histograms are feature vectors consisting of histograms of chosen filter responses, which capture both texture and nontexture information. Based on the observation that the local spectral histogram of a pixel location can be approximated through a linear combination of the representative features weighted by the area coverage of each feature, we formulate the segmentation problem as a multivariate linear regression, where the solution is obtained by least squares estimation. Moreover, we propose an algorithm to automatically identify representative features corresponding to different homogeneous regions, and show that the number of representative features can be determined by examining the effective rank of a feature matrix. We present segmentation results on different types of images, and our comparison with another spectral histogram based method shows that the proposed method gives more accurate results.
机译:我们提出了一种新颖的分割带有纹理和非纹理区域的图像的方法。局部频谱直方图是由选定滤波器响应的直方图组成的特征向量,可以捕获纹理和非纹理信息。基于观察到像素位置的局部光谱直方图可以通过代表性特征的线性组合(由每个特征的面积覆盖率加权)来近似,我们将分割问题公式化为多元线性回归,通过最小二乘估计。此外,我们提出了一种算法,可以自动识别与不同同质区域相对应的代表性特征,并表明可以通过检查特征矩阵的有效等级来确定代表性特征的数量。我们在不同类型的图像上呈现分割结果,并将其与另一种基于频谱直方图的方法进行比较表明,该方法可提供更准确的结果。

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