short-paper

Discriminative Latent Feature Space Learning for Cross-Modal Retrieval

Authors:

Xu Tang,

Cheng Deng,

Xinbo GaoAuthors Info & Claims

ICMR '15: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval

Pages 507 - 510

https://doi.org/10.1145/2671188.2749322

Published: 22 June 2015 Publication History

Get Access

Abstract

Cross-modal retrieval has drawn much attention in recent years due to its wide applications. Most of existing methods only focus on relevance but overlook heterogeneity and discrimination of features from different modalities, and how to capture and correlate these heterogeneous features is still challenging in this field. Therefore, we propose a general model which jointly learns a discriminative latent feature space for effective cross-modal retrieval. Concretely, a class-specific dictionary is learned to account for each modality, and all resulting sparse codes are simultaneously mapped into a common feature space that describes and associates the cross-modal data. Moreover, label information is leveraged to discriminate different classes inside the intra-modality data and also merge the same class inside the inter-modality data. Cross-modal retrieval is finally performed over the learned common feature space. The experimental results confirmed that our cross-modal method outperforms several competing methods on two public datasets.

References

[1]

D. M. Blei and M. I. Jordan. Modeling annotated data. In Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 127--134, 2003.

Digital Library

Google Scholar

[2]

D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. The Journal of Machine Learning Research, 3:993--1022, 2003.

Digital Library

Google Scholar

[3]

D. Hardoon, S. Szedmak, and J. Shawe-Taylor. Canonical correlation analysis: An overview with application to learning methods. Neural Computation, 16(12):2639--2664, 2004.

Digital Library

Google Scholar

[4]

D.-A. Huang and Y.-C. F. Wang. Coupled dictionary and feature space learning with applications to cross-domain image synthesis and recognition. In Proceedings of IEEE International Conference on Computer Vision, pages 2496--2503, 2013.

Digital Library

Google Scholar

[5]

J. Mairal, F. Bach, J. Ponce, and G. Sapiro. Online dictionary learning for sparse coding. In Proceedings of the 26th International Conference on Machine Learning, pages 689--696, 2009.

Digital Library

Google Scholar

[6]

J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman. Discriminative learned dictionaries for local image analysis. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pages 1--8. IEEE, 2008.

Google Scholar

[7]

J. Mairal, J. Ponce, G. Sapiro, A. Zisserman, and F. R. Bach. Supervised dictionary learning. In Advances in neural information processing systems, pages 1033--1040, 2009.

Digital Library

Google Scholar

[8]

D. Putthividhy, H. T. Attias, and S. S. Nagarajan. Topic regression multi-modal latent dirichlet allocation for image annotation. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pages 3408--3415, 2010.

Google Scholar

[9]

N. Rasiwasia, J. Costa Pereira, E. Coviello, G. Doyle, G. R. Lanckriet, R. Levy, and N. Vasconcelos. A new approach to cross-modal multimedia retrieval. In Proceedings of the ACM International Conference on Multimedia, pages 251--260, 2010.

Digital Library

Google Scholar

[10]

A. Sharma, A. Kumar, H. Daume, and D. W. Jacobs. Generalized multiview analysis: A discriminative latent space. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pages 2160--2167, 2012.

Digital Library

Google Scholar

[11]

S. Virtanen, Y. Jia, A. Klami, and D. Trevor. Factorized multi-modal topic model. arXiv preprint arXiv: 1210.4920, 2012.

Google Scholar

[12]

F. Wu, Y. Han, X. Liu, J. Shao, Y. Zhuang, and Z. Zhang. The heterogeneous feature selection with structural sparsity for multimedia annotation and hashing: a survey. International Journal of Multimedia Information Retrieval, 1(1):3--15, 2012.

Crossref

Google Scholar

[13]

M. Yang, D. Zhang, and X. Feng. Fisher discrimination dictionary learning for sparse representation. In Proceedings of IEEE International Conference on Computer Vision, pages 543--550, 2011.

Digital Library

Google Scholar

[14]

Y. Zhuang, Y. F. Wang, F. Wu, Y. Zhang, and W. Lu. Supervised coupled dictionary learning with group structures for multi-modal retrieval. In Proceedings of the Twenty-seventh AAAI Conference on Artificial Intelligence, pages 1070--1076, 2013.

Digital Library

Google Scholar

Cited By

View all

Zheng MWang WLi Q(2021)Fine-Grained Image-Text Retrieval via Discriminative Latent Space LearningIEEE Signal Processing Letters10.1109/LSP.2021.306559528(643-647)Online publication date: 2021
https://doi.org/10.1109/LSP.2021.3065595
Yuan XZhong HChen ZZhong FHu Y(2018)Multimedia Feature Mapping and Correlation Learning for Cross-Modal RetrievalInternational Journal of Grid and High Performance Computing10.4018/IJGHPC.201807010310:3(29-45)Online publication date: 1-Jul-2018
https://dl.acm.org/doi/10.4018/IJGHPC.2018070103
Das NMandal DBiswas S(2017)Simultaneous Semi-Coupled Dictionary Learning for Matching in Canonical SpaceIEEE Transactions on Image Processing10.1109/TIP.2017.270785826:8(3995-4004)Online publication date: 1-Aug-2017
https://dl.acm.org/doi/10.1109/TIP.2017.2707858
Show More Cited By

Index Terms

Discriminative Latent Feature Space Learning for Cross-Modal Retrieval
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

Recommendations

Cross-modal Retrieval with Label Completion
MM '16: Proceedings of the 24th ACM international conference on Multimedia

Cross-modal retrieval has been attracting increasing attention because of the explosion of multi-modal data, e.g., texts and images. Most supervised cross-modal retrieval methods learn discriminant common subspaces minimizing the heterogeneity of ...
Multimedia Feature Mapping and Correlation Learning for Cross-Modal Retrieval

This article describes how with the rapid increasing of multimedia content on the Internet, the need for effective cross-modal retrieval has attracted much attention recently. Many related works ignore the latent semantic correlations of modalities in ...
Joint Dictionary Learning and Semantic Constrained Latent Subspace Projection for Cross-Modal Retrieval
CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management

With the increasing of multi-modal data on the internet, cross-modal retrieval has received a lot of attention in recent years. It aims to use one type of data as query and retrieve results of another type. For different modality data, how to reduce ...

Comments

Information & Contributors

Information

Published In

ICMR '15: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval

June 2015

700 pages

ISBN:9781450332743

DOI:10.1145/2671188

General Chairs:
Alex Hauptmann
Carnegie Mellon University, USA
,
Chong-Wah Ngo
City University of Hong Kong, China
,
Xiangyang Xue
Fudan University, China
,
Program Chairs:
Yu-Gang Jiang
Fudan University, China
,
Cees Snoek
University of Amsterdam and Qualcomm Research Netherlands
,
Nuno Vasconcelos
University of California, San Diego, USA

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 June 2015

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Funding Sources

National High Technology Research and Development Program of China
Fundamental Research Funds for the Central Universities
Program for New Century Excellent Talents in University
Key Science and Technology Program of Shaanxi Province China

Conference

ICMR '15

Sponsor:

SIGMM

ICMR '15: International Conference on Multimedia Retrieval

June 23 - 26, 2015

Shanghai, China

Acceptance Rates

ICMR '15 Paper Acceptance Rate 48 of 127 submissions, 38%;

Overall Acceptance Rate 254 of 830 submissions, 31%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
224
Total Downloads

Downloads (Last 12 months)2
Downloads (Last 6 weeks)0

Reflects downloads up to 21 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Zheng MWang WLi Q(2021)Fine-Grained Image-Text Retrieval via Discriminative Latent Space LearningIEEE Signal Processing Letters10.1109/LSP.2021.306559528(643-647)Online publication date: 2021
https://doi.org/10.1109/LSP.2021.3065595
Yuan XZhong HChen ZZhong FHu Y(2018)Multimedia Feature Mapping and Correlation Learning for Cross-Modal RetrievalInternational Journal of Grid and High Performance Computing10.4018/IJGHPC.201807010310:3(29-45)Online publication date: 1-Jul-2018
https://dl.acm.org/doi/10.4018/IJGHPC.2018070103
Das NMandal DBiswas S(2017)Simultaneous Semi-Coupled Dictionary Learning for Matching in Canonical SpaceIEEE Transactions on Image Processing10.1109/TIP.2017.270785826:8(3995-4004)Online publication date: 1-Aug-2017
https://dl.acm.org/doi/10.1109/TIP.2017.2707858
Wang LSun WZhao ZSu F(2017)Modeling intra- and inter-pair correlation via heterogeneous high-order preserving for cross-modal retrievalSignal Processing10.1016/j.sigpro.2016.08.012131:C(249-260)Online publication date: 1-Feb-2017
https://dl.acm.org/doi/10.1016/j.sigpro.2016.08.012

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Cross-modal Retrieval with Label Completion

Multimedia Feature Mapping and Correlation Learning for Cross-Modal Retrieval

Joint Dictionary Learning and Semantic Constrained Latent Subspace Projection for Cross-Modal Retrieval