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A New LBP Variant: Corner Rhombus Shape LBP (CRSLBP).

Ibtissam Al Saidi1, Mohammed Rziza1, Johan Debayle2

  • 1LRIT Laboratory, Rabat IT Center, Faculty of Sciences, Mohammed V University in Rabat, Rabat B.P. 1014, Morocco.

Journal of Imaging
|July 25, 2022
PubMed
Summary

A new Corner Rhombus-Shape Local Binary Pattern (CRSLBP) improves texture classification by analyzing pixel relationships. This robust method outperforms existing techniques on challenging datasets.

Keywords:
feature extractionlocal binary patterntexture classification

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Area of Science:

  • Computer Vision
  • Image Processing
  • Machine Learning

Background:

  • The Local Binary Pattern (LBP) is a common image analysis technique but has limitations in capturing discriminating features due to its reliance on sign information.
  • Existing LBP variants often use a single center pixel, limiting their ability to capture complex local image characteristics.
  • There is a need for improved texture classification methods that are more robust and effective than traditional LBP approaches.

Purpose of the Study:

  • To introduce a novel variant of the Local Binary Pattern (LBP) called Corner Rhombus-Shape LBP (CRSLBP).
  • To enhance texture classification by incorporating both sign and magnitude information.
  • To evaluate the performance and robustness of the proposed CRSLBP descriptor against state-of-the-art methods.

Main Methods:

  • Developed the Corner Rhombus-Shape Local Binary Pattern (CRSLBP) by thresholding pixel neighbors and centers using sign and magnitude information.
  • Utilized an even block region to establish relationships between center pixels and their surrounding neighbors.
  • Evaluated CRSLBP performance on four diverse and challenging texture databases: Outex (TC10, TC12), Brodatz, KTH-TIPSb2, and UMD.

Main Results:

  • Extensive experiments demonstrated the effectiveness of the CRSLBP descriptor in texture classification.
  • The CRSLBP method showed superior robustness compared to existing state-of-the-art (SOTA) texture analysis techniques.
  • The analysis confirmed CRSLBP's ability to capture more discriminating features than standard LBP.

Conclusions:

  • The proposed Corner Rhombus-Shape Local Binary Pattern (CRSLBP) is an effective and robust descriptor for texture classification.
  • CRSLBP addresses limitations of traditional LBP by utilizing both sign and magnitude information and considering neighborhood relationships.
  • The descriptor shows significant promise for various image analysis and machine learning applications requiring accurate texture recognition.