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Contour and Texture Analysis for Image Segmentation

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Abstract

This paper provides an algorithm for partitioning grayscale images into disjoint regions of coherent brightness and texture. Natural images contain both textured and untextured regions, so the cues of contour and texture differences are exploited simultaneously. Contours are treated in the intervening contour framework, while texture is analyzed using textons. Each of these cues has a domain of applicability, so to facilitate cue combination we introduce a gating operator based on the texturedness of the neighborhood at a pixel. Having obtained a local measure of how likely two nearby pixels are to belong to the same region, we use the spectral graph theoretic framework of normalized cuts to find partitions of the image into regions of coherent texture and brightness. Experimental results on a wide range of images are shown.

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Authors and Affiliations

  1. Computer Science Division, University of California at Berkeley, Berkeley, CA, 94720-1776, USA

    Jitendra Malik, Serge Belongie, Thomas Leung & Jianbo Shi

Authors
  1. Jitendra Malik
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  2. Serge Belongie
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  3. Thomas Leung
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  4. Jianbo Shi
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Malik, J., Belongie, S., Leung, T. et al. Contour and Texture Analysis for Image Segmentation. International Journal of Computer Vision 43, 7–27 (2001). https://doi.org/10.1023/A:1011174803800

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