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Composite hashing with multiple information sources

Authors:

Luo SiAuthors Info & Claims

SIGIR '11: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval

Pages 225 - 234

https://doi.org/10.1145/2009916.2009950

Published: 24 July 2011 Publication History

Abstract

Similarity search applications with a large amount of text and image data demands an efficient and effective solution. One useful strategy is to represent the examples in databases as compact binary codes through semantic hashing, which has attracted much attention due to its fast query/search speed and drastically reduced storage requirement. All of the current semantic hashing methods only deal with the case when each example is represented by one type of features. However, examples are often described from several different information sources in many real world applications. For example, the characteristics of a webpage can be derived from both its content part and its associated links.

To address the problem of learning good hashing codes in this scenario, we propose a novel research problem -- Composite Hashing with Multiple Information Sources (CHMIS). The focus of the new research problem is to design an algorithm for incorporating the features from different information sources into the binary hashing codes efficiently and effectively. In particular, we propose an algorithm CHMIS-AW (CHMIS with Adjusted Weights) for learning the codes. The proposed algorithm integrates information from several different sources into the binary hashing codes by adjusting the weights on each individual source for maximizing the coding performance, and enables fast conversion from query examples to their binary hashing codes. Experimental results on five different datasets demonstrate the superior performance of the proposed method against several other state-of-the-art semantic hashing techniques.

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Cited By

Lu XLiu LNing LZhang LMu SZhang H(2024)Multi-Facet Weighted Asymmetric Multi-Modal Hashing Based on Latent Semantic DistributionIEEE Transactions on Multimedia10.1109/TMM.2024.336366426(7307-7320)Online publication date: 2024
https://doi.org/10.1109/TMM.2024.3363664
Zhu LZheng CGuan WLi JYang YShen H(2024)Multi-Modal Hashing for Efficient Multimedia Retrieval: A SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.328292136:1(239-260)Online publication date: Jan-2024
https://doi.org/10.1109/TKDE.2023.3282921
Zhang TXue ZDong YDu JLiang M(2024)A Multi-View Double Alignment Hashing Network with Weighted Contrastive Learning2024 IEEE International Conference on Multimedia and Expo (ICME)10.1109/ICME57554.2024.10687739(1-6)Online publication date: 15-Jul-2024
https://doi.org/10.1109/ICME57554.2024.10687739
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Index Terms

Composite hashing with multiple information sources
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information retrieval
    1. Document representation

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Published In

cover image ACM Conferences

SIGIR '11: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval

July 2011

1374 pages

ISBN:9781450307574

DOI:10.1145/2009916

General Chairs:
Wei-Ying Ma
Microsoft Research Asia, China
,
Jian-Yun Nie
University of Montreal, Canada
,
Program Chairs:
Ricardo Baeza-Yates
Yahoo! Research, Spain
,
Tat-Seng Chua
National University of Singapore
,
W. Bruce Croft
University of Massachusetts, Amherst, USA

Copyright © 2011 ACM.

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. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGIR: ACM Special Interest Group on Information Retrieval

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New York, NY, United States

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Published: 24 July 2011

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SIGIR '11: The 34th International ACM SIGIR conference on research and development in Information Retrieval

July 24 - 28, 2011

Beijing, China

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

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Cited By

Lu XLiu LNing LZhang LMu SZhang H(2024)Multi-Facet Weighted Asymmetric Multi-Modal Hashing Based on Latent Semantic DistributionIEEE Transactions on Multimedia10.1109/TMM.2024.336366426(7307-7320)Online publication date: 2024
https://doi.org/10.1109/TMM.2024.3363664
Zhu LZheng CGuan WLi JYang YShen H(2024)Multi-Modal Hashing for Efficient Multimedia Retrieval: A SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.328292136:1(239-260)Online publication date: Jan-2024
https://doi.org/10.1109/TKDE.2023.3282921
Zhang TXue ZDong YDu JLiang M(2024)A Multi-View Double Alignment Hashing Network with Weighted Contrastive Learning2024 IEEE International Conference on Multimedia and Expo (ICME)10.1109/ICME57554.2024.10687739(1-6)Online publication date: 15-Jul-2024
https://doi.org/10.1109/ICME57554.2024.10687739
Nie XLiu XGuo JWang LYin Y(2023)Supervised Discrete Multiple-Length Hashing for Image RetrievalIEEE Transactions on Big Data10.1109/TBDATA.2022.31619059:1(312-327)Online publication date: 1-Feb-2023
https://doi.org/10.1109/TBDATA.2022.3161905
Li WZhong GLu XPun C(2023)Locality Preserving Multiview Graph Hashing For Large Scale Remote Sensing Image SearchICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP49357.2023.10096369(1-5)Online publication date: 4-Jun-2023
https://doi.org/10.1109/ICASSP49357.2023.10096369
Ma ZWong WZhang L(2023)Binary multi-view clustering with spectral embeddingNeurocomputing10.1016/j.neucom.2023.126733557:COnline publication date: 7-Nov-2023
https://dl.acm.org/doi/10.1016/j.neucom.2023.126733
Zhang XChen MJiao Mzhao Z(2023)Research on Multimodal Retrieval Method Based on Commodity ImagesProceedings of the 9th International Conference on Advanced Intelligent Systems and Informatics 202310.1007/978-3-031-43247-7_46(532-541)Online publication date: 18-Sep-2023
https://doi.org/10.1007/978-3-031-43247-7_46
Zhu LLi JGuan WZhu LLi JGuan W(2023)Composite Multi-modal HashingMulti-modal Hash Learning10.1007/978-3-031-37291-9_4(91-144)Online publication date: 5-Aug-2023
https://doi.org/10.1007/978-3-031-37291-9_4
Chu HZeng HLai HTang Y(2022)Efficient modal-aware feature learning with application in multimodal hashingIntelligent Data Analysis10.3233/IDA-21578026:2(345-360)Online publication date: 14-Mar-2022
https://doi.org/10.3233/IDA-215780
Ma XYang XGao JXu C(2022)The Model May Fit You: User-Generalized Cross-Modal RetrievalIEEE Transactions on Multimedia10.1109/TMM.2021.309188824(2998-3012)Online publication date: 1-Jan-2022
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