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• W2808021379 abstract "Current hashing methods for cross-modal retrieval generally attempt to learn the separate modality-specific transformation matrices to embed multi-modality data into a latent common subspace, and usually ignore the fact that respecting the diversity of multi-modality features in the latent subspace could be beneficial for retrieval improvements. To this, we propose a collaborative subspace graph hashing method (CSGH) to perform a two-stage collaborative learning framework for cross-modal retrieval. Particularly, CSGH first embeds multi-modality data into separate latent subspaces through individual modality-specific transformation matrices, and then connects these latent subspaces to a common Hamming space through a shared transformation matrix. In this framework, CSGH considers the modality-specific neighborhood structure and the cross-modal correlation within multi-modality data through the Laplacian regularization and the graph based correlation constraint, respectively. To solve CSGH, we develop an alternative procedure to optimize it, and fortunately, each sub-problem of CSGH has the elegant analytical solution. Experiments of cross-modal retrieval on Wiki, NUS-WIDE, Flickr25K and Flickr1M datasets show the effectiveness of CSGH compared with the state-of-the-art cross-modal hashing methods." @default.
• W2808021379 created "2018-06-21" @default.
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• W2808021379 date "2018-06-05" @default.
• W2808021379 modified "2023-10-16" @default.
• W2808021379 title "Collaborative Subspace Graph Hashing for Cross-modal Retrieval" @default.
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• W2808021379 doi "https://doi.org/10.1145/3206025.3206042" @default.
• W2808021379 hasPublicationYear "2018" @default.
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