research-article

Supervised hashing with latent factor models

Authors:

Minyi GuoAuthors Info & Claims

SIGIR '14: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval

July 2014

Pages 173 - 182

https://doi.org/10.1145/2600428.2609600

Published: 03 July 2014 Publication History

Abstract

Due to its low storage cost and fast query speed, hashing has been widely adopted for approximate nearest neighbor search in large-scale datasets. Traditional hashing methods try to learn the hash codes in an unsupervised way where the metric (Euclidean) structure of the training data is preserved. Very recently, supervised hashing methods, which try to preserve the semantic structure constructed from the semantic labels of the training points, have exhibited higher accuracy than unsupervised methods. In this paper, we propose a novel supervised hashing method, called latent factor hashing(LFH), to learn similarity-preserving binary codes based on latent factor models. An algorithm with convergence guarantee is proposed to learn the parameters of LFH. Furthermore, a linear-time variant with stochastic learning is proposed for training LFH on large-scale datasets. Experimental results on two large datasets with semantic labels show that LFH can achieve superior accuracy than state-of-the-art methods with comparable training time.

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Pei YWang ZLi NChen HHuang BTu W(2024)Deep Hashing Network With Hybrid Attention and Adaptive Weighting for Image RetrievalIEEE Transactions on Multimedia10.1109/TMM.2023.332819726(4961-4973)Online publication date: 2024
https://doi.org/10.1109/TMM.2023.3328197
Huang JKang PHan NChen YFang XGao HZhou G(2024)Two-Stage Asymmetric Similarity Preserving Hashing for Cross-Modal RetrievalIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.328398436:1(429-444)Online publication date: Jan-2024
https://doi.org/10.1109/TKDE.2023.3283984
Teng SXu TZheng ZWu NZhang WTeng L(2024)Robust Asymmetric Cross-Modal Hashing Retrieval With Dual Semantic EnhancementIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.335249411:3(4340-4353)Online publication date: Jun-2024
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Index Terms

Supervised hashing with latent factor models
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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cover image ACM Conferences

SIGIR '14: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval

July 2014

1330 pages

ISBN:9781450322577

DOI:10.1145/2600428

General Chairs:
Shlomo Geva
Queensland University of Technology
,
Andrew Trotman
University of Dunedin
,
Program Chairs:
Peter Bruza
Queensland University of Technology
,
Charles L.A. Clarke
University of Waterloo
,
Kal Järvelin
University of Tampere

Copyright © 2014 ACM.

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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 July 2014

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National Natural Science Foundation of China
Program for Changjiang Scholars and Innovative Research Team in University of China
Shanghai Excellent Academic Leaders Plan
Ministry of Science and Technology of the People's Republic of China
Science and Technology Commission of Shanghai Municipality

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SIGIR '14

Sponsor:

SIGIR

SIGIR '14: The 37th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 6 - 11, 2014

Queensland, Gold Coast, Australia

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SIGIR '14 Paper Acceptance Rate 82 of 387 submissions, 21%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Pei YWang ZLi NChen HHuang BTu W(2024)Deep Hashing Network With Hybrid Attention and Adaptive Weighting for Image RetrievalIEEE Transactions on Multimedia10.1109/TMM.2023.332819726(4961-4973)Online publication date: 2024
https://doi.org/10.1109/TMM.2023.3328197
Huang JKang PHan NChen YFang XGao HZhou G(2024)Two-Stage Asymmetric Similarity Preserving Hashing for Cross-Modal RetrievalIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.328398436:1(429-444)Online publication date: Jan-2024
https://doi.org/10.1109/TKDE.2023.3283984
Teng SXu TZheng ZWu NZhang WTeng L(2024)Robust Asymmetric Cross-Modal Hashing Retrieval With Dual Semantic EnhancementIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.335249411:3(4340-4353)Online publication date: Jun-2024
https://doi.org/10.1109/TCSS.2024.3352494
Chen YTan JYang ZShi YQin J(2024)Unsupervised multi-perspective fusing semantic alignment for cross-modal hashing retrievalMultimedia Tools and Applications10.1007/s11042-023-18048-083:23(63993-64014)Online publication date: 9-Jan-2024
https://doi.org/10.1007/s11042-023-18048-0
Sabahi FAhmad MSwamy M(2024)RefinerHash: a new hashing-based re-ranking technique for image retrievalMultimedia Systems10.1007/s00530-024-01296-x30:3Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s00530-024-01296-x
Zhang ZZhang Z(2024)Deep Collaborative Graph HashingBinary Representation Learning on Visual Images10.1007/978-981-97-2112-2_6(143-167)Online publication date: 7-Mar-2024
https://doi.org/10.1007/978-981-97-2112-2_6
Zhang ZZhang Z(2024)Ordinal-Preserving Latent Graph HashingBinary Representation Learning on Visual Images10.1007/978-981-97-2112-2_5(111-141)Online publication date: 7-Mar-2024
https://doi.org/10.1007/978-981-97-2112-2_5
Zhang ZZhang Z(2024)Probability Ordinal-Preserving Semantic HashingBinary Representation Learning on Visual Images10.1007/978-981-97-2112-2_4(81-109)Online publication date: 7-Mar-2024
https://doi.org/10.1007/978-981-97-2112-2_4
Zhang ZZhang Z(2024)Inductive Structure Consistent HashingBinary Representation Learning on Visual Images10.1007/978-981-97-2112-2_3(51-80)Online publication date: 7-Mar-2024
https://doi.org/10.1007/978-981-97-2112-2_3
Zhang ZZhang Z(2024)Scalable Supervised Asymmetric HashingBinary Representation Learning on Visual Images10.1007/978-981-97-2112-2_2(17-50)Online publication date: 7-Mar-2024
https://doi.org/10.1007/978-981-97-2112-2_2
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