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Texture Regimes for Entropy-Based Multiscale Image Analysis

  • Sylvain Boltz
  • Frank Nielsen
  • Stefano Soatto
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6313)

Abstract

We present an approach to multiscale image analysis. It hinges on an operative definition of texture that involves a “small region”, where some (unknown) statistic is aggregated, and a “large region” within which it is stationary. At each point, multiple small and large regions co-exist at multiple scales, as image structures are pooled by the scaling and quantization process to form “textures” and then transitions between textures define again “structures.” We present a technique to learn and agglomerate sparse bases at multiple scales. To do so efficiently, we propose an analysis of cluster statistics after a clustering step is performed, and a new clustering method with linear-time performance. In both cases, we can infer all the “small” and “large” regions at multiple scale in one shot.

Keywords

Dictionary Learning Texture Segmentation Critical Scale Dictionary Element Nuisance Factor 
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 2010

Authors and Affiliations

  • Sylvain Boltz
    • 1
    • 2
  • Frank Nielsen
    • 1
  • Stefano Soatto
    • 1
  1. 1.Laboratoire d’InformatiqueÉcole PolytechniquePalaiseau CedexFrance
  2. 2.UCLA Vision LabUniversity of CaliforniaLos Angeles

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