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Characterization of Color Images with Multiscale Monogenic Maxima.

Raphael Soulard, Philippe Carre

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |October 10, 2017
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel feature-based image analysis for color images, using monogenic maxima for efficient contour and color characterization. This method enables accurate image reconstruction without iterative optimization, advancing keypoint analysis.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Signal Analysis

    Background:

    • Existing edge-based image reconstruction methods are limited to greyscale.
    • Marr's paradigm for image analysis has inspired feature-based approaches.
    • Sparse representation is crucial for efficient image characterization.

    Purpose of the Study:

    • To develop a feature-based analysis for full characterization of color images.
    • To propose a novel sparse representation using monogenic concept and wavelet maxima.
    • To enable direct image reconstruction from extracted features without iterative optimization.

    Main Methods:

    • Utilized the monogenic concept and Mallat-Zhong wavelet maxima method for sparse representation.
    • Extracted local features including amplitude, phase, orientation, and ellipse parameters.
    • Developed a direct image reconstruction process by interpolating data in the feature domain.

    Main Results:

    • Monogenic maxima provide efficient contour shape and color characterization.
    • The proposed method achieves accurate image retrieval with a small feature set.
    • Features demonstrate numerical stability to rotation and facilitate image regularization.

    Conclusions:

    • A novel feature-based analysis effectively characterizes color images.
    • Direct image reconstruction from monogenic maxima is accurate and efficient.
    • The extracted features are robust and useful for image regularization and retrieval.