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Analysis of the Multi-Dimensional Scale Saliency Algorithm and Its Application to Texture Categorization

  • Pablo Suau
  • Francisco Escolano
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6218)

Abstract

A new approach for multi-dimensional Scale Saliency (MDSS) was lately introduced. In this approach, the Scale Saliency algorithm by Kadir and Brady is extended to the multi-dimensional domain. The MDSS algorithm is based on alternative entropy and divergence estimation methods whose complexity does not increase exponentially with data dimensionality. However, MDSS has not been applied to any practical problem yet. In this paper we apply the MDSS algorithm to the texture categorization problem, and we provide further experiments in order to assess the suitability of different estimators to the algorithm. We also propose a new divergence measure based on the k-d partition algorithm.

Keywords

Texture Categorization Query Image Data Dimensionality Partition Algorithm Entropy Estimation 
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

  • Pablo Suau
    • 1
  • Francisco Escolano
    • 1
  1. 1.Robot Vision GroupUniversity of AlicanteSpain

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