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• W4386597002 abstract "Cross-modal hashing is a practical approach to solving the problem of large-scale multimedia retrieval. However, there are still specific issues that the current methods cannot solve, such as how to construct binary codes rather than relax them to continuity effectively and how to prevent n × n problem. This paper proposes a novel Asymmetric Scalable Cross-Modal Hashing (ASCMH) to address these issues. It learns a common latent space from the kernelized features of different modalities. It then transforms the similarity matrix optimization to a distance-distance difference minimization problem with the help of semantic labels and common latent space. Additionally, we use an orthogonal constraint of label information to construct hash codes necessary for search accuracy. Extensive experiments on three benchmark datasets show that our ASCMH outperforms the SOTA cross-modal hashing methods." @default.
• W4386597002 created "2023-09-12" @default.
• W4386597002 creator A5005772506 @default.
• W4386597002 creator A5056741906 @default.
• W4386597002 date "2023-10-08" @default.
• W4386597002 modified "2023-09-30" @default.
• W4386597002 title "Asymmetric Scalable Cross-Modal Hashing" @default.
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• W4386597002 doi "https://doi.org/10.1109/icip49359.2023.10222284" @default.
• W4386597002 hasPublicationYear "2023" @default.
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