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A scale- and orientation-adaptive extension of Local Binary Patterns for texture classification.

Sebastian Hegenbart1, Andreas Uhl1

  • 1Department of Computer Sciences, University of Salzburg, Jakob-Haringer Strasse 2, 5020 Salzburg, Austria.

Pattern Recognition
|August 5, 2015
PubMed
Summary

This study introduces scale- and rotation-invariant Local Binary Patterns (LBPs) for improved texture classification. These enhanced LBPs overcome limitations of traditional methods, significantly boosting accuracy in diverse scenarios.

Keywords:
AdaptiveClassificationInvariantLBPRotationScaleScale-spaceTexture

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Area of Science:

  • Computer Vision
  • Image Processing
  • Pattern Recognition

Background:

  • Local Binary Patterns (LBPs) are effective for texture classification.
  • A key limitation of LBPs is their sensitivity to scale and rotation variations.

Purpose of the Study:

  • To develop a scale- and rotation-invariant Local Binary Pattern (LBP) computation.
  • To enhance the discriminative power of LBP features for texture classification.

Main Methods:

  • Achieved rotation-invariance through explicit feature alignment and robust global orientation estimation.
  • Computed scale-adapted features using scale-normalized Laplacian responses in scale-space.
  • Incorporated intrinsic-scale-adaptation for features independent of texture scale.
  • Combined rotation- and scale-invariant features in a multi-resolution representation.

Main Results:

  • The proposed method generates rotation- and scale-invariant LBP features.
  • Intrinsic-scale-adaptation significantly increases discriminative power across various texture classes.
  • The multi-resolution representation notably improves classification accuracy under scaling and rotation.

Conclusions:

  • The developed scale- and rotation-invariant LBP offers a robust solution for texture classification.
  • This approach significantly enhances performance in scenarios with geometric transformations.
  • The method provides a more discriminative feature representation for complex textures.