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Synergistic Instance-Level Subspace Alignment for Fine-Grained Sketch-Based Image Retrieval.

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    This summary is machine-generated.

    This study introduces a new method for fine-grained sketch-based image retrieval, bridging the domain gap between sketches and photos using part-level attributes and domain alignment. The approach effectively matches sketches to images at an instance level, improving retrieval accuracy.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Fine-grained sketch-based image retrieval (SBIR) is challenging due to domain differences (sketches vs. photos) and abstraction levels.
    • Instance-level retrieval is a practical application, especially with touchscreen devices, but faces difficulties in cross-domain comparisons.
    • Existing methods struggle with abstract sketch representations and the inherent visual disparities between line drawings and photographic images.

    Purpose of the Study:

    • To develop a robust method for fine-grained, instance-level sketch-based image retrieval.
    • To address the challenges posed by the abstract nature of sketches and the domain gap between sketches and photos.
    • To improve the accuracy of retrieving specific images based on hand-drawn sketches.

    Main Methods:

    • A new dataset of 304 photos and 912 sketches, annotated with semantic parts and attributes, was created.
    • Developed supervised deformable part-based models for automatic part-level attribute detection and pose-aligned comparisons.
    • Proposed a novel instance-level domain alignment method using subspace and instance-level cues to reduce the sketch-image feature gap.
    • Integrated aligned low-level features, mid-level geometric structure, and high-level semantic attributes into a unified matching framework.

    Main Results:

    • The proposed method effectively bridges the gap between sketch and image domains at both high-level (parts, attributes) and low-level (feature alignment).
    • Experiments on the new dataset demonstrate the effectiveness of the integrated matching framework.
    • The approach enables more accurate pose-aligned comparisons between sketches and images.

    Conclusions:

    • The developed method significantly enhances fine-grained sketch-based image retrieval performance.
    • Combining semantic attributes, geometric structure, and aligned low-level features is crucial for effective cross-domain retrieval.
    • The annotated dataset and proposed techniques provide a strong foundation for future research in SBIR.