K-Means Hashing: An Affinity-Preserving Quantization Method for Learning Binary Compact Codes

By Kaiming He, Fang Wen. 2013

414 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Representation & Self-Supervised Learning

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Citations in the corpus

Billion-Scale Similarity Search with GPUs (2017, background): “The most popular vector compression methods can be classified into either binary codes [18, 22], or quantization methods [25, 37].”
Deep Hashing Network for Unsupervised Domain Adaptation (2017, methodology): “Unsupervised hashing techniques have been developed to extract unique hash codes for efficient storage and retrieval of data [22, 25].”
Learning Discrete Representations via Information Maximizing Self-Augmented Training (2017, methodology): “The representative ones include K-means clustering and hashing (He et al., 2013), Gaussian mixture model clustering, iterative quantization (Gong et al., 2013), and minimal-loss hashing (Norouzi & Blei, 2011).”
Polysemous Codes (2016, methodology): “Related to our work, the k-means hashing method [24] ﬁrst produces a vector quantizer where the produced codes are compared with the Hamming distance.”
Multiscale Quantization for Fast Similarity Search (2017, background): “Similar ideas are also reported in recent literature [10, 17, 2].”
Hashing for Distributed Data (2015, methodology): “, 2012), k-means Hashing (KMH) (He et al., 2013) and (Liu et al.”
Latent semantic sparse hashing for cross-modal similarity search (2014, background): “Moreover, several literatures take the quantization of Hamming space into account, and have achieved superior results, such as K-means Hashing [8], ITQ Hashing [7] and Double-Bit Hashing [12].”
Deep hashing for compact binary codes learning (2015, methodology): “Table 4 shows the training and test time of different hashing methods on the CIFAR-10 dataset when 16-bit binary codes are used for evaluation , where the test time is computed for each query 0 200 400 600 800 1000 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 N Recall ITQ KMH Spherical SH PCAH DH (a) 16 bits 0”
Optimized Product Quantization for Approximate Nearest Neighbor Search (2013, methodology): “…search on large-scale datasets, taking only a fraction of a second for each nearest neighbor query [18, 10] Hashing [1, 18, 20, 19, 6, 8] has been a popular approach to compact encoding, where the similarity between two data points is approximated by the Hamming distance of their hashed codes.”
Learning to Hash for Indexing Big Data—A Survey (2015, background): “learning [25], clustering analysis [26], semi-supervised learning [14], supervised learning [27][22], graph learning [28], and so on.”
Accelerating Large-Scale Inference with Anisotropic Vector Quantization (2019, methodology): “Quantization methods of this form are available for binary quantization (He et al., 2013; Liong et al., 2015; Dai et al., 2017), product quantization (Jegou et al., 2011; Guo et al., 2016b; Zhang et al., 2014; Wu et al., 2017), additive quantization (Babenko & Lempitsky, 2014; Martinez et al.,…”
Feature Learning Based Deep Supervised Hashing with Pairwise Labels (2015, background): “…has become one of the most popular and effective techniques due to its fast query speed and low memory cost [Kulis and Grauman, 2009; Gong and Lazebnik, 2011; Kong and Li, 2012; Liu et al., 2012; Rastegari et al., 2013; He et al., 2013; Lin et al., 2014; Shen et al., 2015; Kang et al., 2016].”
Collective Matrix Factorization Hashing for Multimodal Data (2014, methodology): “As extensions of standard LSH, some machine learning methods are employed to design effective compact hashing, such as Manifold Learning, Supervised Learning, Kernel Learning, Deep Learning, Quantization Learning, Linear Discriminant Analysis (LDA), K-means and PCA, which respectively generate Spect”
Online sketching hashing (2015, methodology): “These sketches may be useful for providing online version for other hashing methods such as [11, 12].”
Progressive Generative Hashing for Image Retrieval (2018, methodology): “Despite the progress in the linear projection based hashing, these methods still cannot well capture the nearest neighbor relations using the binary codes, mainly due to the fact that the shallow structures lack the capability of modelling complex data distribution [He et al., 2013].”
Semantics-preserving hashing for cross-view retrieval (2015, neutral): “Existing hashing methods can be roughly classiﬁed into two categories: single-view hashing [16, 21, 4, 11, 6, 13, 8, 22, 10] and multi-view hashing [1, 9, 20, 24, 23, 18, 27, 14, 3, 25, 17, 26, 19], with the former focusing on data with a single view while the latter focusing on that with multiple…”