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Neutro-Connectedness Cut.

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    This study introduces NC-Cut, a novel interactive image segmentation method. NC-Cut overcomes limitations of existing approaches by using neutro-connectedness (NC) properties for improved accuracy and reduced user interaction.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Computational Topology

    Background:

    • Interactive image segmentation faces challenges with ROI sensitivity and extensive user interaction.
    • Existing methods often struggle with accurate object delineation due to these limitations.

    Purpose of the Study:

    • To develop a robust interactive image segmentation method that overcomes current drawbacks.
    • To introduce a novel approach utilizing neutro-connectedness (NC) for improved segmentation.

    Main Methods:

    • Generalized neutro-connectedness (NC) to model image topology with indeterminacy.
    • Developed a method for determining object/background regions, excluding isolated regions and enforcing consistency.
    • Proposed NC-Cut, a hybrid method combining pixelwise appearance and region-based NC properties.

    Main Results:

    • NC-Cut effectively overcomes ROI sensitivity and reduces the need for intense user interaction.
    • Evaluated on two datasets (265 images), demonstrating superior performance compared to state-of-the-art methods.
    • Achieved better segmentation accuracy and consistency than Grabcut, MILCut, One-Cut, MGCmaxsum, and pPBC.

    Conclusions:

    • NC-Cut offers a significant advancement in interactive image segmentation.
    • The method provides a more reliable and efficient solution for segmenting objects in images.
    • Neutro-connectedness is a valuable tool for modeling image topology in segmentation tasks.