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    This study introduces Inductive Structure Consistent Hashing (ISCH) to improve multimedia retrieval by bridging visual, semantic, and hashing gaps. ISCH effectively coordinates these spaces for enhanced accuracy in image and data retrieval tasks.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Semantic-preserving hashing aims for efficient multimedia retrieval by encoding data similarities into hash codes.
    • A key challenge is bridging the gaps between visual, semantic, and hashing spaces to boost retrieval accuracy.

    Purpose of the Study:

    • To propose an Inductive Structure Consistent Hashing (ISCH) method for improved multimedia retrieval.
    • To effectively coordinate semantic correlations across visual, class, and hashing spaces.

    Main Methods:

    • Formulated an inductive semantic space using a multilayer stacking class-encoder for flexible semantic embeddings.
    • Designed a semantic dictionary learning model to bridge visual-semantic gaps and guide the class-encoder.
    • Employed a coinciding alignment module to regularize visual descriptors and semantic class representations.
    • Utilized semantic and prototype binary code learning for unified discrete hash codes.
    • Developed an efficient optimization algorithm for the discrete programming problem.

    Main Results:

    • Demonstrated the superiority of ISCH over state-of-the-art methods on large-scale datasets (CIFAR-10, NUSWIDE, ImageNet, MSCOCO).
    • Achieved improved retrieval accuracy by effectively bridging the trilateral domain gaps.

    Conclusions:

    • ISCH offers a novel approach to semantic-preserving hashing by interactively coordinating diverse data spaces.
    • The method effectively addresses the visual-semantic bias problem and enhances multimedia retrieval performance.