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Fast Cosine Similarity Search in Binary Space with Angular Multi-index Hashing [article]

Sepehr Eghbali, Ladan Tahvildari
2018 arXiv   pre-print
To address this issue, we propose the angular multi-index hashing search algorithm which relies on building multiple hash tables on binary code substrings.  ...  Given a large dataset of binary codes and a binary query point, we address how to efficiently find K codes in the dataset that yield the largest cosine similarities to the query.  ...  FAST COSINE SIMILARITY SEARCH To reduce the search cost, we propose to use a hash table populated with binary codes.  ... 
arXiv:1610.00574v2 fatcat:3acfnvuttzejplba2b7hkfnu2a

Survey Paper on Generating Correlation among Different Modalities by Using Parallel Processing for Cross-Media Retrieval

Rokkam SrikanthReddy
2017 International Journal for Research in Applied Science and Engineering Technology  
In the proposed method retrieve cross-media information using multi core processer and multi-threading.  ...  Hashing methods are useful for performing variety of tasks in recent years. Various hashing approaches have been performing retrieve the cross-media information.  ...  Angular Quantization-based Binary Codes for Fast Similarity Search Y. Gong, S. Kumar, V. Verma, and S.  ... 
doi:10.22214/ijraset.2017.8309 fatcat:5j65haeztfdedoljvqrczo2pxq

Angular Quantization Online Hashing for Image Retrieval

Yuzhi Fang, Li Liu
2021 IEEE Access  
Online hash method with fast search mechanism and compact index structure plays a pivotal role.  ...  In this article, we propose a new method called Angular Quantization Online Hashing (AQOH) to focus on learning compact binary codes with the help of cosine distance.  ...  Compact binary codes not only have better resolution performance, but also facilitate fast similarity calculations, making them usable for fast nearest neighbor search.  ... 
doi:10.1109/access.2021.3095367 fatcat:sljqybso7nbgjii5fwgcqfhm4q

Learning to Hash for Indexing Big Data - A Survey [article]

Jun Wang, Wei Liu, Sanjiv Kumar, Shih-Fu Chang
2015 arXiv   pre-print
The explosive growth in big data has attracted much attention in designing efficient indexing and search methods recently.  ...  Importantly, the learned hash codes are able to preserve the proximity of neighboring data in the original feature spaces in the hash code spaces.  ...  Here simp¨,¨q represents similarity between a pair of points in the input space, e.g., cosine similarity or Jaccard similarity [34] .  ... 
arXiv:1509.05472v1 fatcat:haj52w3cbbgszlmalfyu2kvzde

Fast Exact Search in Hamming Space with Multi-Index Hashing [article]

Mohammad Norouzi, Ali Punjani, David J. Fleet
2014 arXiv   pre-print
We introduce a rigorous way to build multiple hash tables on binary code substrings that enables exact k-nearest neighbor search in Hamming space.  ...  There is growing interest in representing image data and feature descriptors using compact binary codes for fast near neighbor search.  ...  ACKNOWLEDGMENTS This research was financially supported in part by NSERC Canada, the GRAND Network Centre of Excellence, and the Canadian Institute for Advanced Research (CIFAR).  ... 
arXiv:1307.2982v3 fatcat:ntijza35bja47jpxzlvntcf5py

A Survey on Learning to Hash [article]

Jingdong Wang, Ting Zhang, Jingkuan Song, Nicu Sebe, Heng Tao Shen
2017 arXiv   pre-print
and space cost.  ...  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  ...  Inverted Multi-Index Hash table lookup with binary hash codes is a form of inverted index.  ... 
arXiv:1606.00185v2 fatcat:j5mnu7lfmvby5pfkg5pffk2nae

Fast Exact Search in Hamming Space With Multi-Index Hashing

Mohammad Norouzi, Ali Punjani, David J. Fleet
2014 IEEE Transactions on Pattern Analysis and Machine Intelligence  
We introduce a rigorous way to build multiple hash tables on binary code substrings that enables exact k-nearest neighbor search in Hamming space.  ...  There is growing interest in representing image data and feature descriptors using compact binary codes for fast near neighbor search.  ...  ACKNOWLEDGMENTS This research was financially supported in part by NSERC Canada, the GRAND Network Centre of Excellence, and the Canadian Institute for Advanced Research (CIFAR).  ... 
doi:10.1109/tpami.2013.231 pmid:26353274 fatcat:zpziarqtt5cxjfadmndbyr3uju

Efficient Retrieval of Matrix Factorization-Based Top-k Recommendations: A Survey of Recent Approaches

Dung D. Le, Hady Lauw
2021 The Journal of Artificial Intelligence Research  
In this work, we survey recent advances and state-of-the-art approaches in the literature that enable fast and accurate retrieval for MF-based personalized recommendations.  ...  An established methodology in the literature based on matrix factorization (MF), which usually represents users and items as vectors in low-dimensional space, is an effective approach to recommender systems  ...  NSW (Malkov & Yashunin, 2019) is proposed to take advantage of the Delaunay Graph, the NSWN, and the Relative Neighborhood Graphs, enabling multi-scale hopping on different layers of the graph.  ... 
doi:10.1613/jair.1.12403 fatcat:fpum5xffmbhclme3hdmmbs34uy

Hashing for Similarity Search: A Survey [article]

Jingdong Wang, Heng Tao Shen, Jingkuan Song, Jianqiu Ji
2014 arXiv   pre-print
according the data distribution, and review them from various aspects, including hash function design and distance measure and search scheme in the hash coding space.  ...  Similarity search (nearest neighbor search) is a problem of pursuing the data items whose distances to a query item are the smallest from a large database.  ...  Fast Search in Hamming Space Multi-index hashing The idea [104] is that binary codes in the reference database are indexed M times into M different hash tables, based on M disjoint binary substrings  ... 
arXiv:1408.2927v1 fatcat:reknwesjnbafvcbouyudrzp4rq

Non-Metric Space Library Manual [article]

Bilegsaikhan Naidan, Leonid Boytsov, Yury Malkov, David Novak
2019 arXiv   pre-print
This document covers a library for fast similarity (k-NN)search. It describes only search methods and distances (spaces).  ...  NMSLIB is possibly the first library with a principled support for non-metric space searching.  ...  In the binary version of the multi-vantage point tree (MVP-tree), which is implemented in NMSLIB, there are two pivots.  ... 
arXiv:1508.05470v4 fatcat:crze3blt3rbwhkay53mbjubobi

Comparing apples to apples in the evaluation of binary coding methods [article]

Mohammad Rastegari, Shobeir Fakhraei, Jonghyun Choi, David Jacobs, Larry S. Davis
2014 arXiv   pre-print
These coding methods attempt to preserve either Euclidean distance or angular (cosine) distance in the binary embedding space.  ...  To compare a method whose goal is to preserves Euclidean distance to one that preserves cosine similarity, the original feature data must be mapped to a higher dimension by including a bias term in binary  ...  [15] proposed a multi-index hashing method, and Rastegari et al.  ... 
arXiv:1405.1005v2 fatcat:slj2dr4exjb4tasmloerz3tpiy

IHashNet: Iris Hashing Network based on efficient multi-index hashing [article]

Avantika Singh, Chirag Vashist, Pratyush Gaurav, Aditya Nigam, Rameshwar Pratap
2020 arXiv   pre-print
Finally, for indexing the iris dataset, we have proposed a loss that can transform the binary feature into an improved feature compatible with the Multi-Index Hashing scheme.  ...  Here, in this paper, we propose an iris indexing scheme using real-valued deep iris features binarized to iris bar codes (IBC) compatible with the indexing structure.  ...  that transformed binary iris features into an improved feature compatible with Multi-Index Hashing scheme.  ... 
arXiv:2012.03881v1 fatcat:li6k77apcjfypju4q2jqlod25e

A Review of Hashing Methods for Multimodal Retrieval

Wenming Cao, Wenshuo Feng, Qiubin Lin, Guitao Cao, Zhihai He
2020 IEEE Access  
With the advent of the information age, the amount of multimedia data has exploded. That makes fast and efficient retrieval in multimodal data become an urgent requirement.  ...  Among many retrieval methods, the hashing method is widely used in multimodal data retrieval due to its low storage cost, fast and effective characteristics.  ...  At the same time, the similarity of the data should be maintained in the mapping process (the data with high similarity in the original space is mapped to the Hamming space, and the distance between the  ... 
doi:10.1109/access.2020.2968154 fatcat:e3vmte5hrnhu3b3lf5ws4gwnhm

A Survey on Deep Hashing Methods [article]

Xiao Luo, Haixin Wang, Daqing Wu, Chong Chen, Minghua Deng, Jianqiang Huang, Xian-Sheng Hua
2022 arXiv   pre-print
Nearest neighbor search aims to obtain the samples in the database with the smallest distances from them to the queries, which is a basic task in a range of fields, including computer vision and data mining  ...  We also introduce three related important topics including semi-supervised deep hashing, domain adaption deep hashing and multi-modal deep hashing.  ...  We also thank Zeyu Ma, Huasong Zhong and Xiaokang Chen who discussed with us and provided instructive suggestions.  ... 
arXiv:2003.03369v5 fatcat:m2iu3htilvgztkcazw3cyk6iqe

Scalable Multi-grained Cross-modal Similarity Query with Interpretability

Mingdong Zhu, Derong Shen, Lixin Xu, Xianfang Wang
2021 Data Science and Engineering  
In this work, we investigate multi-grained common semantic embedding representations of images and texts and integrate interpretable query index into the deep neural network by developing a novel Multi-grained  ...  Cross-modal Query with Interpretability (MCQI) framework.  ...  The preliminary version of this article has been published in APWeb-WAIM 2020 [https:// doi. org/ 10. 1007/ 978-3-030-60290- 1_ 26]  ... 
doi:10.1007/s41019-021-00162-4 fatcat:7tdgbtoq2jc45ixrdltrl4nofu
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