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Gray-level invariant Haralick texture features.

Tommy Löfstedt1, Patrik Brynolfsson1, Thomas Asklund1

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Summary
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This study introduces invariant Haralick texture features, overcoming issues with image quantization. These new features provide reproducible texture analysis, improving classification accuracy regardless of gray-level reduction.

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Area of Science:

  • Image analysis and computer vision.
  • Texture analysis and feature extraction.
  • Digital image processing.

Background:

  • Haralick texture features are widely used but sensitive to image quantization.
  • Quantization, the reduction of gray-levels, impacts feature reproducibility.
  • Lack of invariance hinders consistent texture analysis across different datasets.

Purpose of the Study:

  • To develop Haralick texture features invariant to the number of gray-levels used in quantization.
  • To enhance the reproducibility and robustness of texture analysis.
  • To improve classification performance in image analysis tasks.

Main Methods:

  • Redefined the gray-level co-occurrence matrix (GLCM) as a discretized probability density function.
  • Developed a novel method for computing asymptotically invariant Haralick features.
  • Employed logistic regression for classification to compare feature sets.

Main Results:

  • Invariant Haralick features demonstrated asymptotic invariance to quantization levels.
  • Classifiers trained on invariant features achieved higher classification accuracies.
  • Performance remained consistent even with significant differences in quantization between training and testing data.

Conclusions:

  • The proposed invariant Haralick features ensure consistent texture analysis irrespective of image quantization.
  • This approach significantly improves the reliability and applicability of Haralick features in image analysis.
  • Invariant features offer a robust solution for texture classification tasks.