Journal Article10.1109/TPAMI.2017.2699960
A Survey on Learning to Hash
TL;DR: In this paper, a comprehensive survey of the learning to hash algorithms is presented, categorizing them according to the manners of preserving the similarities into: pairwise similarity preserving, multi-wise similarity preservation, implicit similarity preserving and quantization, and discuss their relations.
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Abstract: Nearest neighbor search is a problem of finding the data points from the database such that the distances from them to the query point are the smallest. Learning to hash is one of the major solutions to this problem and has been widely studied recently. In this paper, we present a comprehensive survey of the learning to hash algorithms, categorize them according to the manners of preserving the similarities into: pairwise similarity preserving, multiwise similarity preserving, implicit similarity preserving, as well as quantization, and discuss their relations. We separate quantization from pairwise similarity preserving as the objective function is very different though quantization, as we show, can be derived from preserving the pairwise similarities. In addition, we present the evaluation protocols, and the general performance analysis, and point out that the quantization algorithms perform superiorly in terms of search accuracy, search time cost, and space cost. Finally, we introduce a few emerging topics.
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Citations
Robust image hashing for content identification through contrastive self-supervised learning
TL;DR: In this article , the authors exploit the recent advances in self-supervised learning to generate a feature representation by solving a metric learning-based pretext task that enforces the robust image hashing properties for content identification systems.
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Perceptual Vibration Hashing by Sub-Band Coding: An Edge Computing Method for Condition Monitoring
TL;DR: Perceptual hashing is proposed as an edge computing form, aiming not only to reduce the data dimensionality but also to extract and represent the machine condition information.
•Posted Content
Deep Class-Wise Hashing: Semantics-Preserving Hashing via Class-wise Loss
Xuefei Zhe,Shifeng Chen,Hong Yan +2 more
TL;DR: This model is motivated by deep metric learning that directly takes semantic labels as supervised information in training and generates corresponding discriminant hashing code that preserves semantic variations while penalizes the overlapping part of different classes in the embedding space.
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Unsupervised Multi-Index Semantic Hashing
Christian Hansen,Casper Worm Hansen,Jakob Grue Simonsen,Stephen Alstrup,Christina Lioma +4 more
- 19 Apr 2021
TL;DR: Multi-Index Semantic Hashing (MISH) as discussed by the authors is an unsupervised hashing model that learns hash codes that are both effective and highly efficient by being optimized for multi-index hashing.
OPHiForest: Order Preserving Hashing Based Isolation Forest for Robust and Scalable Anomaly Detection
Haolong Xiang,Zoran Salcic,Wanchun Dou,Xiaolong Xu,Lianyong Qi,Xuyun Zhang +5 more
- 19 Oct 2020
TL;DR: The core idea is to learn the information from data to construct better isolation forest structure than the state-of-the-art methods like iForest and LSHiForest, which can achieve robust detection of various anomaly types.
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