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Summary

This study introduces a novel color texture representation that combines multiple color spaces and descriptors. This approach significantly enhances classification accuracy, outperforming existing methods, especially with limited data.

Keywords:
chromatic cooccurrence matrixcolor local binary pattern histogramcolor spacescolor texture representationdimensionality reductionfeature selectiontexture classification

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Area of Science:

  • Computer Vision
  • Image Processing
  • Machine Learning

Background:

  • Traditional color texture classification relies on predefined parameters and single color spaces, limiting performance.
  • Selecting appropriate color spaces, descriptors, and settings is a critical challenge impacting classification accuracy.
  • Existing methods often struggle with optimal feature representation for diverse texture patterns.

Purpose of the Study:

  • To develop a robust color texture representation that integrates information from multiple color spaces and descriptor settings.
  • To address the limitations of predetermined configurations in color texture analysis.
  • To create a compact and effective descriptor for improved classification.

Main Methods:

  • Proposed a novel color texture representation by combining features from multiple color spaces and descriptor settings.
  • Implemented a dimensionality reduction scheme using clustering-based sequential feature selection.
  • Developed a compact hybrid multi-color space (CHMCS) descriptor.

Main Results:

  • The CHMCS representation achieved an average accuracy of 94.16% across five benchmark databases.
  • Demonstrated superior performance compared to traditional handcrafted and deep learning methods, exceeding them by over 5%.
  • Showcased particular effectiveness on small datasets.

Conclusions:

  • Combining diverse configurations of color spaces and texture descriptors significantly improves classification accuracy.
  • The proposed CHMCS descriptor offers a powerful and compact solution for color texture representation.
  • This method provides a more adaptable and accurate approach to color texture classification, especially in data-scarce scenarios.