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Learning How to Extract Rotation-Invariant and Scale-Invariant Features from Texture Images

Abstract

Learning how to extract texture features from noncontrolled environments characterized by distorted images is a still-open task. By using a new rotation-invariant and scale-invariant image descriptor based on steerable pyramid decomposition, and a novel multiclass recognition method based on optimum-path forest, a new texture recognition system is proposed. By combining the discriminating power of our image descriptor and classifier, our system uses small-size feature vectors to characterize texture images without compromising overall classification rates. State-of-the-art recognition results are further presented on the Brodatz data set. High classification rates demonstrate the superiority of the proposed system.

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Correspondence to Javier A. Montoya-Zegarra.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Montoya-Zegarra, J.A., Papa, J.P., Leite, N.J. et al. Learning How to Extract Rotation-Invariant and Scale-Invariant Features from Texture Images. EURASIP J. Adv. Signal Process. 2008, 691924 (2008). https://doi.org/10.1155/2008/691924

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Keywords

  • Pyramid
  • Texture Feature
  • Classification Rate
  • Texture Image
  • Recognition Result