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- W4288049649 abstract "To maintain the semantic similarity of an image due to information redundancy of the hash code generated by the currently popular hash-retrieval method, this paper proposes a multi-dimensional equilibrium-depth hash method. With its multi-dimensional input, the method effectively improves the learning effect of the grids for image features, optimises the loss function, and considers the quantisation error and equilibrium of the hash code while keeping the semantic similarity to improve the quality of the hash codes. In a verification experiment on Flickr and NUS-WIDE datasets, the proposed method achieved higher retrieval accuracy than other hash methods." @default.
- W4288049649 created "2022-07-27" @default.
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- W4288049649 date "2022-02-26" @default.
- W4288049649 modified "2023-10-16" @default.
- W4288049649 title "A Multi-dimensional Equilibrium-depth Hash Image Retrieval Method" @default.
- W4288049649 cites W2134514757 @default.
- W4288049649 doi "https://doi.org/10.1145/3524383.3524408" @default.
- W4288049649 hasPublicationYear "2022" @default.
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