Kernelized Locality-Sensitive Hashing

By Brian Kulis, Kristen Grauman. 2012

355 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

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Citations in the corpus

Optimal Rates for Random Fourier Features (2015, positive): “In this paper, we provide the first detailed theoretical analysis about the approximation quality of RFFs by establishing optimal (in terms of the RFF dimension) performance guarantees in uniform and Lr (1 ≤ r ∞) norms.”
Unsupervised Semantic-Preserving Adversarial Hashing for Image Search (2019, background): “Since exact nearest neighbor search is typically time-consuming and unsuitable for large-scale datasets, approximate nearest neighbor (ANN) search [8], [9], which attempts to find the nearest neighbor with a high probability, has been actively studied and successfully applied to many machine learnin”
Optimized Cartesian K-Means (2014, methodology): “Generally, we use the uppercase unbolded symbol as a constant, the lowercase unbolded as the index, the uppercase bolded as the matrix and the lowercase bolded as the vector.”
Hashing for Similarity Search: A Survey (2014, neutral): “Kernel LSH [64], [65] aims to build LSH functions with the angle defined in the kernel space, θ(xi,xj) = arccos φ(xi) φ(xj) ‖φ(xi)‖2‖φ(xj)‖2 .”
Two-Stream Deep Hashing With Class-Specific Centers for Supervised Image Search (2020, background): “S IMILARITY search is a fundamental research topic in numerous computer vision and multimedia applications, including object recognition [1], information retrieval [2]–[19], image classiﬁcation [20], [21], motion estimation [22], and image understanding [23], [24].”
Supervised hashing with kernels (2012, methodology): “Following the Kernelized LocalitySensitive Hashing (KLSH) [6] algorithm, we first define a prediction function f : R → R with the kernel κ plugged in as follows”
Hashing with binary autoencoders (2015, methodology): “Related work The most basic hashing approaches are data-independent, such as Locality-Sensitive Hashing (LSH) [1], which is based on random projections and thresholding, and kernelized LSH [17].”
Top Rank Supervised Binary Coding for Visual Search (2015, background): “Note that although nonlinear binary coding or hashing algorithms [8, 13] may be more effective for image search, we limit the compared algorithms to be linear approaches for fair comparison (we notice that the SH algorithm, though nonlinear, is based on and very similar to linear PCA hashing).”
Discrete Graph Hashing (2014, methodology): “The F-measures of KLSH, IsoH, SH and BRE deteriorate quickly and are with very poor values ( 0.05) when r ≥ 48 due to poor recall 3 .”
Learning to Hash for Indexing Big Data—A Survey (2015, methodology): “Other interesting learning ideas include twostep learning methods which treat hash bit learning and hash function learning separately [85][86].”