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Measuring Just-noticeable Differences Using Shape Size Changes
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Structure descriptor based on just noticeable difference for texture image classification.

Xikui Miao, Wei Zhao, Xiaolong Li

    Applied Optics
    |September 11, 2019
    PubMed
    Summary
    This summary is machine-generated.

    A novel texture classification method uses just noticeable difference (JND) to create a JND visual pattern (JNDVP) descriptor. This JNDVP method is more robust to noise and outperforms traditional Local Binary Pattern (LBP) methods.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Machine Learning

    Background:

    • Local Binary Pattern (LBP) is a common texture classification method.
    • LBP is sensitive to noise and can lose structural information.
    • Existing methods require improvements in robustness and detail preservation.

    Purpose of the Study:

    • To propose a new local structure descriptor for texture classification.
    • To enhance robustness against noise and improve information preservation.
    • To introduce a descriptor that is rotation invariant.

    Main Methods:

    • Computed a Just Noticeable Difference (JND) map for images.
    • Modeled local neighborhood pixel correlations using JND thresholds.
    • Developed a Just Noticeable Difference Visual Pattern (JNDVP) descriptor.
    • Incorporated image contrast as a weighting factor for JNDVP histogram creation.
    • Utilized a nearest neighborhood classifier for texture classification.

    Main Results:

    • The proposed JNDVP descriptor demonstrated rotation invariance.
    • JNDVP showed superior robustness to noise compared to LBP.
    • Texture classification using JNDVP outperformed LBP-based methods.
    • The method effectively captures both structural and contrast information.

    Conclusions:

    • The JNDVP descriptor offers a robust and effective approach to texture classification.
    • This method overcomes limitations of traditional LBP techniques.
    • JNDVP shows significant potential for applications requiring noise-resilient texture analysis.