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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Color texture classification using shortest paths in graphs.

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    This study introduces a new graph-based method for analyzing color textures. The approach models images as graphs, achieving high classification accuracy on benchmark datasets for robust texture analysis.

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    Area of Science:

    • Computer Vision
    • Image Analysis
    • Pattern Recognition

    Background:

    • Color textures are crucial visual features in image analysis.
    • Existing methods for color texture analysis have limitations.
    • Developing robust and accurate texture analysis techniques is essential.

    Purpose of the Study:

    • To propose a novel graph-based method for color texture analysis.
    • To model color images as graphs using complementary approaches.
    • To extract statistical moments from shortest paths for feature vector creation.

    Main Methods:

    • Representing color images as graphs in two distinct ways: per color channel and combined channels.
    • Calculating statistical moments from shortest paths between graph vertices.
    • Extracting feature vectors from VisTex, USPTex, and TC00013 color texture databases.

    Main Results:

    • Achieved high classification success rates across three benchmark databases.
    • Reported best results including 99.07% (LDA, leave-one-out) on VisTex.
    • Demonstrated strong performance with 91.54% (LDA, leave-one-out) on TC00013.

    Conclusions:

    • The proposed graph-based method is a powerful tool for color texture analysis.
    • The approach offers a novel perspective on texture representation and feature extraction.
    • The method shows significant potential for various image analysis applications.