Image segmentation using local spectral histograms and linear regression

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 other methods shows that the proposed method gives more accurate results.

► The segmentation problem is formulated as a multivariate linear regression. ► Accurate segmentation can be efficiently obtained by least squares estimation. ► The representative features of homogeneous regions are automatically identified. ► The effective rank of a feature matrix indicates the number of segments.