Supervised hashing with kernels

By Wei David Liu, Jun Wang, Rongrong Ji, Yu-Gang Jiang, Shih-Fu Chang. 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012

1,483 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/88ed6b8d4209bb0e113b1fc77a4924f79e412a0e

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In Computer Vision and Pattern Recognition

1. Dacheng Tao
2. Luc Van Gool
3. Yi Yang
4. Xiaogang Wang
5. Jiashi Feng
6. Ming-Hsuan Yang
7. Ling Shao
8. Chunhua Shen

Citations in the corpus

• Latent semantic sparse hashing for cross-modal similarity search (2014, methodology): “…as Boosting algorithm, Restricted Boltzmann Machines, Manifold Learning, Supervised Learning, Kernel Learning and PCA, which respectively generate Parameter sensitive Hashing [26], Semantic Hashing [25], Spectral Hashing [30], Su-pervised Hashing [16], Kernelized Hashing [13] and PCA Hashing [29].”
• 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…”
• DeepHash: Getting Regularization, Depth and Fine-Tuning Right (2015, background): “There is plenty of work on binary codes for descriptors like SIFT or GIST [9, 15, 16, 17, 10, 12, 18, 11, 19, 20, 21].”
• Neural Decision Trees (2017, methodology): “Learning this kind of function in a supervised manner has been studied Liu et al. (2012).”
• Bit-Scalable Deep Hashing With Regularized Similarity Learning for Image Retrieval and Person Re-Identification (2015, background): “On the other hand, most existing hashing learning approaches generate the hashing codes with preset lengths ( e.g. , 16, 32 or 64 bits) [5][7][12], but one often requires hashing codes of different lengths under different scenarios.”
• Neural information retrieval: at the end of the early years (2017, background): “However, semantic hashing difficult optimization procedures and a slow training mechanism, reducing applicability to large-scale tasks (Liu et al. 2012).”
• Fast Exact Search in Hamming Space With Multi-Index Hashing (2013, neutral): “Others focus on semantic similarity ( e.g., [25], [32], [31], [35], [26], [30], [21]).”
• Variational Deep Semantic Hashing for Text Documents (2017, methodology): “…in the prior work [36]: Locality Sensitive Hashing (LSH) 6 [2], Spectral Hashing (SpH) 7 [38], Self-taught Hashing (STH) 8 [41], Stacked Restricted Boltzmann Machines (Stacked RBMs) [28], Supervised Hashing with Kernels (KSH) [20], and Semantic Hashing using Tags and Topic Modeling (SHTTM) [36].”
• Deep Supervised Discrete Hashing (2017, methodology): “Supervised Hashing with Kernels (KSH) [12] is a well-known method of this kind, which learns the hash codes by minimizing the Hamming distances between similar pairs, and at the same time maximizing the Hamming distances between dissimilar pairs.”
• Large-Scale Supervised Multimodal Hashing with Semantic Correlation Maximization (2014, methodology): “…and Indyk 2008; Kulis and Darrell 2009; Lin, Ross, and Yagnik 2010; Wang, Kumar, and Chang 2010; Norouzi and Fleet 2011; Kong, Li, and Guo 2012; Heo et al. 2012; Liu et al. 2012a; Kong and Li 2012b; Norouzi, Fleet, and Salakhutdinov 2012; Strecha et al. 2012; Ge et al. 2013; Neyshabur et al. 2013).”
• NASH: Toward End-to-End Neural Architecture for Generative Semantic Hashing (2018, methodology): “For supervised semantic hashing, we also compare NASH against a number of baselines: Supervised Hashing with Kernels (KSH) (Liu et al., 2012), Semantic Hashing using Tags and Topic Modeling (SHTTM) (Wang et al., 2013) and Supervised VDSH (Chaidaroon and Fang, 2017).”
• Deep Hashing Network for Efficient Similarity Retrieval (2016, methodology): “Supervised Hashing with Kernels (KSH) (Liu et al. 2012) is a kernel-based method that builds compact binary codes by minimizing the Hamming distances on similar pairs and maximizing the Hamming distances on dissimilar pairs.”
• Top Rank Supervised Binary Coding for Visual Search (2015, methodology): “In particular, pointwise supervised methods (e.g., BRE [7]) and pairwise supervised methods (e.g., MLH [16] and KSH [13]) respectively leverage labels of instances and pairwise labels of instance-pairs to train the coding functions such that the label information can be preserved in the produced Ham”
• The Power of Asymmetry in Binary Hashing (2013, methodology): “precision-recall curve. In all of our experiments, in addition to Minimal Loss Hashing (MLH), we also compare our approach to three other widely used methods: Kernel-Based Supervised Hashing (KSH) of [6], Binary Reconstructive Embedding (BRE) 6 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 0.2 0.4 0.6”
• Learning in high-dimensional multimedia data: the state of the art (2015, background): “64 bits 128 bits BRE [44] High low SSH [92] low low ITQ [23] fair fair KSH [53] High fair FastHash [50] High There are also some further developments extended from the standard hashing algorithms: Multiple Feature Hashing (MFH) [81] learns a group of hash functions to generate binary codes for multi”
• 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].”
• Iterative Quantization: A Procrustean Approach to Learning Binary Codes for Large-Scale Image Retrieval (2013, neutral): “For example, Liu et al. [24] use a spectral embedding, He et al. [15] use a method similar to independent component analysis (ICA), and Strecha et al. [43] use linear discriminant analysis (LDA).”
• 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”