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• W2790970504 abstract "Hashing techniques with asymmetric schemes (e.g., only bi-narizing the database points) have recently attracted wide attention in the circle of image retrieval. In comparison with those methods which binarize simultaneously both of the query and database points, they not only enjoy the storage and search efficiencies, but also provide higher accuracy. Gearing to this line, this paper proposes a metric-embedded asymmetric hashing (MEAH) that learns jointly a bilinear similarity measure and binary codes of database points in an unsupervised manner. Technically, the learned similarity measure is able to bridge the gap between the binary codes and the real-valued codes, which are represented possibly with different dimensions. What is more, this measure is capable of preserving the global structure hidden in the database. Extensive experiments on two public image benchmarks demonstrate the superiority of our approach over the several state-of-the-art unsupervised hashing methods." @default.
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• W2790970504 date "2017-09-01" @default.
• W2790970504 modified "2023-09-22" @default.
• W2790970504 title "Efficient similarity learning for asymmetric hashing" @default.
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• W2790970504 doi "https://doi.org/10.1109/icip.2017.8296404" @default.
• W2790970504 hasPublicationYear "2017" @default.
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