Compositional Correlation Quantization for Large-Scale Multimodal Search
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Summary
This paper establishes seamless multimodal hashing by proposing a novel Compositional Correlation Quantization (CCQ) model, which jointly finds correlation-maximal mappings that transform different modalities into an isomorphic latent space, and learns compositional quantizers that quantize the isomorph latent features into compact binary codes.
- Type
- preprint
- Published
- 2015-04-19
- Cited by
- 5
- References
- 38
- Access
- Open access
- OpenAlex
- https://openalex.org/W1780444626
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:16962218
Keywords
Computer science, Nearest neighbor search, Hash function, Quantization (signal processing), Binary code
References
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- Multimodal learning with deep Boltzmann machines
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- Semantics-preserving hashing for cross-view retrieval
- Deep semantic ranking based hashing for multi-label image retrieval
- Cross-modal Retrieval with Correspondence Autoencoder
- Data fusion through cross-modality metric learning using similarity-sensitive hashing
- Additive Quantization for Extreme Vector Compression
- Iterative Multi-View Hashing for Cross Media Indexing
- Discriminative coupled dictionary hashing for fast cross-media retrieval
- NUS-WIDE: a real-world web image database from National University of Singapore
- Near-Optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions
- A low rank structural large margin method for cross-modal ranking
- The Inverted Multi-Index
- Inter-media hashing for large-scale retrieval from heterogeneous data sources
- A generalized solution of the orthogonal procrustes problem
- A probabilistic model for multimodal hash function learning
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