Tensor Processing for Texture and Colour Segmentation

  • Rodrigo de Luis-García
  • Rachid Deriche
  • Mikael Rousson
  • Carlos Alberola-López
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3540)


In this paper, we propose an original approach for texture and colour segmentation based on the tensor processing of the nonlinear structure tensor. While the tensor structure is a well established tool for image segmentation, its advantages were only partly used because of the vector processing of that information. In this work, we use more appropriate definitions of tensor distance grounded in concepts from information theory and compare their performance on a large number of images. We clearly show that the traditional Frobenius norm-based tensor distance is not the most appropriate one. Symmetrized KL divergence and Riemannian distance intrinsic to the manifold of the symmetric positive definite matrices are tested and compared. Adding to that, the extended structure tensor and the compact structure tensor are two new concepts that we present to incorporate gray or colour information without losing the tensor properties. The performance and the superiority of the Riemannian based approach over some recent studies are demonstrated on a large number of gray-level and colour data sets as well as real images.


Colour Image Segmentation Method Geodesic Distance Structure Tensor Texture Segmentation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Rodrigo de Luis-García
    • 1
  • Rachid Deriche
    • 2
  • Mikael Rousson
    • 2
  • Carlos Alberola-López
    • 1
  1. 1.ETSI TelecomunicaciónUniversity of ValladolidValladolidSpain
  2. 2.Projet OdysséeINRIA Sophia-AntipolisFrance

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