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Dynamic Texture Analysis and Synthesis Using Tensor Decomposition

  • Roberto Costantini
  • Luciano Sbaiz
  • Sabine Süsstrunk
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4292)

Abstract

Dynamic textures are sequences of images showing temporal regularity, such as smoke, flames, flowing water, or moving grass. Despite being a multidimensional signal, existing models reshape the dynamic texture into a 2D signal for analysis. In this article, we propose to directly decompose the multidimensional (tensor) signal, free from reshaping operations. We show that decomposition techniques originally applied to study psychometric or chemometric data can be used for this purpose. Since spatial, time, and color information are analyzed at the same time, such techniques permit to obtain more compact models. Only one third or less model coefficients are needed for the same quality and synthesis cost of 2D based models, as illustrated by experiments on real dynamic textures.

Keywords

Singular Value Decomposition Texture Synthesis Dynamic Texture Tensor Decomposition Multidimensional Signal 
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 2006

Authors and Affiliations

  • Roberto Costantini
    • 1
  • Luciano Sbaiz
    • 1
  • Sabine Süsstrunk
    • 1
  1. 1.School of Computer and Communication SciencesEcole Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland

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