Locality-sensitive binary codes from shift-invariant kernels

By Maxim Raginsky, Svetlana Lazebnik. Neural Information Processing Systems, 2009

707 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/1b20afbd2d2a349737ed3dc246e44bbdba203190

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Citations in the corpus

Progressive Generative Hashing for Image Retrieval (2018, background): “Although the nonlinear mapping techniques like kernel can help uplift the data into an informative space [Raginsky and Lazebnik, 2009; Kulis and Darrell, 2009; Liu et al. , 2011], they are usually time-consuming, and mean-while still fail to exploit the underlying data structures using the linear…”
Iterative quantization: A procrustean approach to learning binary codes (2011, methodology): “We use a Gaussian kernel with bandwidth set to the average distance to the 50th nearest neighbor as in [12].”
Hashing for Similarity Search: A Survey (2014, background): “Locality sensitive binary coding using shift invariant kernel hashing [109] exploits the property that the binary mapping of the original data is guaranteed to preserve the value of a shift-invariant kernel, the random Fourier features (RFF) [110].”
Deep Cross-Modal Hashing (2016, background): “…to its low storage cost and fast retrieval speed, hashing has recently attracted much attention from the ANN research community (Weiss et al., 2008; Raginsky & Lazebnik, 2009; Wang et al., 2010; Liu et al., 2011; Norouzi & Fleet, 2011; Norouzi et al., 2012; Rastegari et al., 2013; Yu et al., 2014;…”
Optimized Cartesian K-Means (2014, unknown): “C V ] 16 M ay 2 01 4 Index Terms—Clustering, Cartesian product, Nearest neighbor search ✦”
Minimal Loss Hashing for Compact Binary Codes (2011, methodology): “…for binary reconstructive embedding – BRE (Kulis & Darrell, 2009), spectral hashing – SH (Weiss et al., 2008), shift-invariant kernel hashing – SIKH (Raginsky & Lazebnik, 2009), and multilayer neural nets with semantic fine-tuning – NNCA (Torralba et al., 2008), were obtained with…”
Large-scale image retrieval with compressed Fisher vectors (2010, background): “There has been a lot of work recently on computing compact binary codes with application mainly to GIST descriptors [19], including [25] [26] [16] [23].”
Polysemous Codes (2016, background): “Many variants have been subsequently proposed, such as spectral hashing [52] or ITQ [21] – see also [43,55,50] for representative works.”
Efficient On-the-fly Category Retrieval Using ConvNets and GPUs (2014, unknown): “Despite the excellent progress in compression methods for nearest neighbour search by using product quantization [15] or binary encoding [16], [17], compromises still had to be made.”
Hamming Distance Metric Learning (2012, background): “, [26]) provide guarantees that metric similarity is preserved for sufficiently long codes.”
Learning Binary Codes for Maximum Inner Product Search (2015, background): “The LSH family has been continuously developed to accommodate a variety of distance and similarity measures, such as Euclidean distance, p-norm distance [3], Mahalanobis distance [15], kernel similarity [14, 28].”
Complementary Incremental Hashing With Query-Adaptive Re-Ranking for Image Retrieval (2020, methodology): “The Locality Sensitive Hashing [5] and its variants, such as SKLSH [6], KLSH [7], and LSH for Chi 2 distance [8], are representative unsupervised methods which generate hash functions randomly.”
Query-driven iterated neighborhood graph search for large scale indexing (2012, background): “The best-ﬁrst strategy [3] is usually adopted for local neighborhood expansion.”
Semi-Supervised Hashing for Large-Scale Search (2012, background): “Also, they have achieved state-of-the-art performance for various tasks [12][13][14].”
Fast search in Hamming space with multi-index hashing (2012, methodology): “, [5, 12, 18, 21, 24]), while others focus on semantic similarity (e.”
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”
Fast Exact Search in Hamming Space With Multi-Index Hashing (2013, unknown): “Nearest neighbor (NN) search on binary codes is used for image search [29], [35], [38], matching local features [2], [7], [16], [33], image classiﬁcation [6], object segmentation [19], and parameter estimation [32].”