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    This study introduces a click-based deep interactive image matting (DIIM) approach, simplifying user input for precise foreground and background extraction. DIIM offers a flexible and efficient alternative to traditional trimap-based methods.

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    Area of Science:

    • Computer Vision
    • Image Processing

    Background:

    • Traditional image matting often requires trimaps, which are labor-intensive and inflexible for user interaction.
    • Existing trimap-free methods have limited versatility, often restricted to specific scenarios like human matting.

    Purpose of the Study:

    • To develop a more user-friendly and versatile image matting method using interactive clicks.
    • To enhance alpha matte estimation through novel feature propagation and extraction techniques.

    Main Methods:

    • Proposed a Deep Interactive Image Matting (DIIM) approach utilizing sparse user clicks for foreground/background indication.
    • Introduced a recurrent alpha feature propagation module for high-level feature enhancement.
    • Developed a full-resolution extraction module for low-level feature refinement.

    Main Results:

    • The click-based DIIM approach demonstrated promising performance on standard image matting datasets.
    • Clicks offer a simpler, more flexible interaction method compared to trimaps, especially for novice users.
    • The method allows for gradual error correction and user-satisfied predictions through interactive operations.

    Conclusions:

    • The proposed DIIM method offers a significant improvement in user interaction and flexibility for image matting.
    • This approach broadens the applicability of interactive image matting beyond specific scenarios.
    • DIIM provides an effective and efficient solution for generating high-quality alpha mattes.