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International Journal of Computer Vision

, Volume 95, Issue 1, pp 86–98 | Cite as

Segmentation of Natural Images by Texture and Boundary Compression

  • Hossein Mobahi
  • Shankar R. Rao
  • Allen Y. YangEmail author
  • Shankar S. Sastry
  • Yi Ma
Article

Abstract

We present a novel algorithm for segmentation of natural images that harnesses the principle of minimum description length (MDL). Our method is based on observations that a homogeneously textured region of a natural image can be well modeled by a Gaussian distribution and the region boundary can be effectively coded by an adaptive chain code. The optimal segmentation of an image is the one that gives the shortest coding length for encoding all textures and boundaries in the image, and is obtained via an agglomerative clustering process applied to a hierarchy of decreasing window sizes as multi-scale texture features. The optimal segmentation also provides an accurate estimate of the overall coding length and hence the true entropy of the image. We test our algorithm on the publicly available Berkeley Segmentation Dataset. It achieves state-of-the-art segmentation results compared to other existing methods.

Keywords

Image segmentation Texture segmentation Minimum description length 

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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Hossein Mobahi
    • 1
  • Shankar R. Rao
    • 2
  • Allen Y. Yang
    • 3
    Email author
  • Shankar S. Sastry
    • 3
  • Yi Ma
    • 1
    • 4
  1. 1.Coordinated Science LabUniversity of IllinoisUrbanaUSA
  2. 2.HRL LaboratoriesLLCMalibuUSA
  3. 3.Cory Hall, Department of EECSUniversity of CaliforniaBerkeleyUSA
  4. 4.Visual Computing GroupMicrosoft Research AsiaBeijingChina

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