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Retrieval is the process of getting information out of memory storage and back into conscious awareness. This ability is essential for daily tasks like brushing hair and teeth, driving to work, and performing job duties. Retrieval occurs in three ways: recall, recognition, and relearning.
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Cross-Modal Multivariate Pattern Analysis
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Asymmetric Supervised Consistent and Specific Hashing for Cross-Modal Retrieval.

Min Meng, Haitao Wang, Jun Yu

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |November 24, 2020
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    Summary

    This study introduces Asymmetric Supervised Consistent and Specific Hashing (ASCSH), a novel method for cross-modal hashing. ASCSH effectively leverages both consistent and modality-specific information for improved multimedia data similarity search.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Hashing techniques offer efficient solutions for cross-modal similarity search in large multimedia datasets.
    • Existing cross-modal hashing (CMH) methods fail to simultaneously utilize consistent and modality-specific information and often neglect pairwise similarity due to computational costs.
    • Relaxation-based strategies in CMH lead to quantization errors and suboptimal solutions by converting discrete problems into continuous ones.

    Purpose of the Study:

    • To propose a novel supervised cross-modal hashing method, Asymmetric Supervised Consistent and Specific Hashing (ASCSH).
    • To address the limitations of existing CMH methods by exploiting both consistent and modality-specific information and incorporating pairwise similarity.
    • To overcome the drawbacks of relaxation-based approaches by developing a discrete asymmetric framework that avoids quantization errors.

    Main Methods:

    • ASCSH explicitly decomposes mapping matrices into consistent and modality-specific components to capture inter-modal correlations.
    • A discrete asymmetric framework is proposed, jointly formulating pairwise similarity and semantic labels to guide hash code learning.
    • The method solves the binary constraint problem discretely and efficiently without relaxation, unlike existing asymmetric approaches.

    Main Results:

    • Extensive experiments were conducted on three widely used datasets to evaluate the effectiveness of ASCSH.
    • The proposed ASCSH method demonstrated superior performance compared to state-of-the-art CMH methods.
    • The results validate the effectiveness of simultaneously exploiting consistent and modality-specific information and the discrete asymmetric framework.

    Conclusions:

    • ASCSH offers a significant advancement in supervised cross-modal hashing by effectively integrating consistent and modality-specific information.
    • The novel discrete asymmetric framework provides an efficient and accurate solution for learning hash codes without relaxation-induced errors.
    • The superior performance on benchmark datasets confirms ASCSH's potential for practical applications in cross-modal similarity search.