Image segmentation using local spectral histograms and linear regression

  • Authors:
  • Jiangye Yuan;DeLiang Wang;Rongxing Li

  • Affiliations:
  • Mapping and GIS Laboratory, Department of Civil and Environmental Engineering and Geodetic Science, Columbus, OH 43210, United States;Department of Computer Science and Engineering, Center for Cognitive Science, The Ohio State University, Columbus, OH 43210, United States;Mapping and GIS Laboratory, Department of Civil and Environmental Engineering and Geodetic Science, Columbus, OH 43210, United States

  • Venue:
  • Pattern Recognition Letters
  • Year:
  • 2012

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Abstract

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.