Discrete Graph Hashing

By Wei David Liu, Cun Mu, Sanjiv Kumar, Shih-Fu Chang. Neural Information Processing Systems, 2014

587 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/67a2a783ef4bda774876c37bf9826341e33017f9

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Citations in the corpus

Binary Generative Adversarial Networks for Image Retrieval (2017, result): “We also compare with PCAH [42], DGH [29], AGH [31], and UN-BDNH [6] in term of precision.”
Feature Learning Based Deep Supervised Hashing with Pairwise Labels (2015, methodology): “Representative unsupervised methods include iterative quantization (ITQ) [3], anchor graph hashing (AGH) [19], isotropic hashing (IsoHash) [7], and discrete graph hashing (DGH) [17].”
Creating Something From Nothing: Unsupervised Knowledge Distillation for Cross-Modal Hashing (2020, background): “To scale up these models to real-world scenarios, researchers often compressed the output of these models into binary vectors of a fixed length [1, 19, 10, 24], i.”
Triplet-Based Deep Hashing Network for Cross-Modal Retrieval (2018, methodology): “The larger the MAP, the better the retrieval performance.”
Unsupervised Semantic-Preserving Adversarial Hashing for Image Search (2019, methodology): “DGH uses a discrete optimization framework to solve the graph hashing problem and explicitly deals with the discrete constraints, meaning that it can directly output binary codes.”
A Survey on Learning to Hash (2016, background): “There aremany other extensions in awide range, including similarity graph extensions [75], [179], [92], [86], [84], [79], [128], [170], hash function extensions [40], [124], weighted Hamming distance [153], self-taught hashing [166], sparse hash codes [177], discrete hashing [164], and so on.”
Unsupervised Deep Hashing with Similarity-Adaptive and Discrete Optimization (2018, methodology): “In this work, we are particularly interested in the well-known graph hashing problem [29], [31], [49]”
Deep Supervised Hashing for Fast Image Retrieval (2016, methodology): “Methods such as Discrete Graph Hashing (DGH) [16] and Supervised Discrete Hashing (SDH) [23] are proposed to directly optimize the binary codes to overcome the shortcomings of relaxation, and achieves improved retrieval performance.”
Learning Visual Semantic Relationships for Efficient Visual Retrieval (2015, methodology): “We introduce these techniques in the following part.”
Self-Supervised Adversarial Hashing Networks for Cross-Modal Retrieval (2018, methodology): “Most existing shallow cross-modal hashing methods (in both unsupervised [2, 10, 14, 18] and supervised settings [7, 17, 19, 26, 30, 40, 33]), always capture the semantic relevance in a common Hamming space.”
Deep Sketch Hashing: Fast Free-Hand Sketch-Based Image Retrieval (2017, methodology): “Related Work Hashing techniques [42, 16, 34, 62, 39, 30, 55, 35, 17, 14, 74, 36, 14, 70, 37, 47, 38, 54, 25, 33, 41] have recently been successfully applied to encode highdimensional features into compact similarity-preserving binary codes, which enables extremely fast similarity search by the use o”
Two-Stream Deep Hashing With Class-Specific Centers for Supervised Image Search (2020, methodology): “Various unsupervised hashing methods have been proposed in recent years [38]–[41], [45], [46].”
Learning Binary Codes for Maximum Inner Product Search (2015, background): “More recently, Anchor Graph Hashing (AGH) [20, 18] leverages anchor graphs for making hash code training and out-of-sample extension to novel data both tractable and efficient.”
Top Rank Supervised Binary Coding for Visual Search (2015, methodology): “Unsupervised approaches, such as Spectral Hashing (SH) [29], Iterative Quantization (ITQ) [4], Isotropic Hashing (ISOH) [6], Discrete Graph Hashing (DGH) [12] etc.”
Learning to Hash for Indexing Big Data—A Survey (2015, methodology): “The anchor graph hashing approach was recently further improved by leveraging a discrete optimization technique to directly solve binary hash codes without any relaxation [98].”
Discrete Collaborative Filtering (2016, background): “Latest improvements on joint optimizations of quantization losses and intrinsic objective functions can be found in Discrete Graph Hashing [17] and Supervised Discrete Hashing [26], which demonstrate signiﬁcant performance gain over the above two-stage hashing methods.”
High-Dimensional Sparse Cross-Modal Hashing with Fine-Grained Similarity Embedding (2021, methodology): “Typical examples include Iterative Quantization (ITQ) [13], Discrete Graph Hashing (DGH) [30], Discrete Locality Linear Embedding (DLLH) [18], Supervised Hashing with Kernels (KSH) [31], Super-vised Discrete Hashing (SDH) [41], Discrete Hashing with Multiple Supervision (MSDH) [34], Deep Semantic Re”