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Cross-Modal Multivariate Pattern Analysis
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Generalized Semantic Preserving Hashing for Cross-Modal Retrieval.

Devraj Mandal, Kunal N Chaudhury, Soma Biswas

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |August 7, 2018
    PubMed
    Summary
    This summary is machine-generated.

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    This study introduces a flexible hashing framework for cross-modal retrieval, effectively handling various data scenarios and capturing semantic relationships. The approach optimizes hash codes and learns hash functions, proving useful across diverse datasets.

    Area of Science:

    • Computer Science
    • Information Retrieval
    • Machine Learning

    Background:

    • Multimedia data is rapidly increasing, necessitating efficient retrieval methods.
    • Hashing techniques offer a scalable solution for large-scale cross-modal retrieval.
    • Existing methods face challenges with diverse label associations and data correspondences.

    Purpose of the Study:

    • To propose a unified hashing framework for cross-modal retrieval adaptable to various data scenarios.
    • To effectively capture semantic relationships between data items from different modalities.
    • To address challenges posed by single-label, multi-label, and varied correspondence data.

    Main Methods:

    • A two-stage hashing framework is proposed.
    • Stage one: Factorizing an affinity matrix to learn optimal hash codes.

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  • Stage two: Employing ridge regression and kernel logistic regression to learn hash functions for mapping data to the bit domain.

    • Main Results:

    • The framework demonstrates effectiveness across single-label (Wiki) and multi-label (MirFlickr, NUS-WIDE, Pascal, LabelMe) datasets.
    • The proposed approach shows competitive performance compared to state-of-the-art methods.
    • A novel iterative solution is presented for handling very large or incomplete training data.

    Conclusions:

    • The proposed hashing framework is versatile and effective for cross-modal retrieval tasks.
    • It successfully captures semantic relationships and handles diverse data labeling scenarios.
    • The iterative solution enhances scalability for large-scale machine learning applications.