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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Combination of LBP Bin and Histogram Selections for Color Texture Classification.

Alice Porebski1, Vinh Truong Hoang2, Nicolas Vandenbroucke1

  • 1LISIC laboratory, Université du Littoral Côte d'Opale, 50 rue Ferdinand Buisson, 62228 Calais CEDEX, France.

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|August 30, 2021
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Summary

This study introduces a novel method for reducing feature space dimensions in Local Binary Pattern (LBP) analysis. Combining LBP bin and histogram selection significantly improves texture classification accuracy compared to independent methods.

Keywords:
color spacesfeature selectionlocal binary pattern descriptortexture classification

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Area of Science:

  • Computer Vision
  • Image Processing
  • Machine Learning

Background:

  • Local Binary Pattern (LBP) is a widely used texture descriptor in computer vision.
  • LBP histograms often result in high-dimensional feature spaces, posing challenges for color texture classification.
  • Existing methods focus on feature selection of LBP bins or entire histograms to reduce dimensionality.

Purpose of the Study:

  • To propose an improved approach for reducing the dimensionality of LBP feature spaces.
  • To enhance texture classification performance by combining LBP bin and histogram selection strategies.
  • To evaluate the effectiveness of the proposed combined selection method.

Main Methods:

  • A novel approach combining LBP bin and histogram selection is presented.
  • A histogram ranking method is applied prior to LBP bin selection.
  • The proposed method was evaluated on five benchmark image databases.

Main Results:

  • The combined LBP bin and histogram selection approach demonstrates superior performance.
  • The proposed method outperforms independent LBP bin selection and LBP histogram selection techniques.
  • Effectiveness was validated across multiple benchmark datasets.

Conclusions:

  • The combination of LBP bin and histogram selection is an effective strategy for dimensionality reduction.
  • This integrated approach yields better texture classification results than previous standalone methods.
  • The findings suggest a more efficient and accurate way to utilize LBP features.