Sequential Projection Learning for Hashing with Compact Codes

By Jun Wang, Sanjiv Kumar, Shih-Fu Chang. International Conference on Machine Learning, 2010

341 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/8dbf5b2addb3cfbc8c72f15d65fe234268a628e6

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Citations in the corpus

Linear cross-modal hashing for efficient multimedia search (2013, background): “…Search Process Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.”
Iterative Quantization: A Procrustean Approach to Learning Binary Codes for Large-Scale Image Retrieval (2013, unknown): “Another family of methods uses dimensionality reduction prior to binary coding, with PCA being the most common choice [13], [14], [18], [49], [50], [51].”
Iterative quantization: A procrustean approach to learning binary codes (2011, methodology): “For this case, we report the averaged precision of top 500 ranked images for each query as in [20].”
Hashing with Graphs (2011, methodology): “All training and test time is recorded in sec. r = 48 ℓ2 Scan 0.4125 – 0.4523 – SE ℓ2 Scan 0.5269 0.3909 – – 0.4866 0.4775 – – LSH 0.1613 0.2196 1.8 2.1×10 −5 0.3196 0.2844 8.5 1.0×10 −5 PCAH 0.2596 0.2242 4.5 2.2×10 −5 0.3643 0.3450 18.8 1.3×10 −5 USPLH 0.4699 0.4930 163.2 2.3×10 −5 0.4269 0.4322 8”
Fast Exact Search in Hamming Space With Multi-Index Hashing (2013, unknown): “Some vision applications restrict search to exact matches ( i.e., r = 0 ) or a small search radius ( e.g., [14], [37] ), but in most cases of interest the desired search radius is larger than is currently feasible ( e.g., see Fig.”
Deep Cross-Modal Hashing (2016, background): “Due 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., 201”
Learning to Hash for Indexing Big Data—A Survey (2015, methodology): “Another effective hash code ranking method is the query-sensitive hashing, which explores the raw feature of the query sample and learns query-specific weights of hash bits to achieve accurate ǫnearest neighbor search [94].”
Deep Supervised Discrete Hashing (2017, methodology): “Supervised hashing methods include SPLH [15], KSH [12], FastH [10], LFH [22], and SDH [14].”
Minimal Loss Hashing for Compact Binary Codes (2011, background): “Wang et al. (2010) extended SH to incorporate discrete supervision where sets of similar and dissimilar training points are available.”
Hamming Distance Metric Learning (2012, methodology): “Under this mapping the kth bit is determined by a hyperplane in the input space whose normal is given by the kth row of W .”
A Survey on Learning to Hash (2016, background): “Semi-supervised hashing [141], [142], [143] is the representative algorithm in this group.”
Large-Scale Supervised Multimodal Hashing with Semantic Correlation Maximization (2014, methodology): “…existing hashing methods are unimodal (Weiss, Torralba, and Fer-gus 2008; Andoni 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,…”
Hashing for Similarity Search: A Survey (2014, background): “On the other hand, as an inspiration of LSH, the concept of “small code” or “compact code” has become the focus of many researchers from the CV community, and many learning-based hashing methods have come in to being [135][125][126][30][83][139][127][29][82][130][31].”
Isotropic Hashing (2012, methodology): “SPL leans the projection functions in a sequential way that each function is designed to correct the errors caused by the previous one.”
Circulant Binary Embedding (2014, unknown): “R = circ(r) := r0 rd−1 . . . r2 r1 r1 r0 rd−1 r2 ... r1 r0 . . . ... rd−2 . . . . . . rd−1 rd−1 rd−2 . . . r1 r0”
Feature Learning Based Deep Supervised Hashing with Pairwise Labels (2015, methodology): “Representative pairwise label based methods include sequential projection learning for hashing (SPLH) [Wang et al., 2010], minimal loss hashing (MLH) [Norouzi and Fleet, 2011], supervised hashing with kernels (KSH) [Liu et al., 2012], two-step hashing (TSH) [Lin et al., 2013], fast supervised hashin”
Semi-Supervised Hashing for Large-Scale Search (2012, background): “However, in practice, the performance is significantly degraded if only compact codes are used [13][28].”