Cited By
View all- Li ZTang J(2022)A survey on social image semantic analysisChinese Science Bulletin10.1360/TB-2022-093868:25(3368-3384)Online publication date: 11-Nov-2022
Subspace Clustering Based Tag Sharing for Inductive Tag Matrix Refinement with Complex Errors
Pages 1013 - 1016
Abstract
Annotating images with tags is useful for indexing and retrieving images. However, many available annotation data include missing or inaccurate annotations. In this paper, we propose an image annotation framework which sequentially performs tag completion and refinement. We utilize the subspace property of data via sparse subspace clustering for tag completion. Then we propose a novel matrix completion model for tag refinement, integrating visual correlation, semantic correlation and the novelly studied property of complex errors. The proposed method outperforms the state-of-the-art approaches on multiple benchmark datasets even when they contain certain levels of annotation noise.
References
[1]
G. Carneiro, A. Chan, P. Moreno, and N. Vasconcelos. Supervised learning of semantic classes for image annotation and retrieval. TPAMI, 2007.
[2]
M. Chen, A. Zheng, and K. Weinberger. Fast image tagging. In ICML, 2013.
[3]
F. Chung. Spectral graph theory. 1997.
[4]
J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell. Decaf: A deep convolutional activation feature for generic visual recognition. arXiv preprint arXiv:1310.1531, 2013.
[5]
E. Elhamifar and R. Vidal. Sparse subspace clustering. In CVPR, 2009.
[6]
E. Elhamifar and R. Vidal. Clustering disjoint subspaces via sparse representation. In ICASSP, 2010.
[7]
S. Feng, R. Manmatha, and V. Lavrenko. Multiple Bernoulli relevance models for image and video annotation. In CVPR, 2004.
[8]
Z. Feng, S. Feng, R. Jin, and A. Jain. Image tag completion by noisy matrix recovery. In ECCV. 2014.
[9]
Z. Feng, R. Jin, and A. Jain. Large-scale image annotation by efficient and robust kernel metric learning. In ICCV, 2013.
[10]
A. Goldberg, B. Recht, J. Xu, R. Nowak, and X. Zhu. Transduction with matrix completion: Three birds with one stone. In NIPS, 2010.
[11]
M. Guillaumin, T. Mensink, J. Verbeek, and C. Schmid. Tagprop: Discriminative metric learning in nearest neighbor models for image auto-annotation. In ICCV, 2009.
[12]
M. Huiskes and M. Lew. The MIR Flickr retrieval evaluation. In ICMIR, 2008.
[13]
P. Jain and I. Dhillon. Provable inductive matrix completion. arXiv preprint arXiv:1306.0626, 2013.
[14]
J. Jeon, V. Lavrenko, and R. Manmatha. Automatic image annotation and retrieval using cross-media relevance models. In SIGIR, 2003.
[15]
X. Li, C. Snoek, and M. Worring. Learning social tag relevance by neighbor voting. 2009.
[16]
X. Li, T. Uricchio, L. Ballan, M. Bertini, C. G. Snoek, and A. Del Bimbo. Socializing the semantic gap: A comparative survey on image tag assignment, refinement and retrieval. arXiv preprint arXiv:1503.08248, 2015.
[17]
Z. Lin, R. Liu, and Z. Su. Linearized alternating direction method with adaptive penalty for low-rank representation. In NIPS, 2011.
[18]
A. Makadia, V. Pavlovic, and S. Kumar. A new baseline for image annotation. In ECCV. 2008.
[19]
T. Mikolov, K. Chen, G. Corrado, and J. Dean. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781, 2013.
[20]
N. Natarajan and I. Dhillon. Inductive matrix completion for predicting gene--disease associations. Bioinformatics, 2014.
[21]
B. Russell, A. Torralba, K. Murphy, and W. Freeman. Labelme: a database and web-based tool for image annotation. IJCV, 2008.
[22]
L. Wu, R. Jin, and A. Jain. Tag completion for image retrieval. TPAMI, 2013.
[23]
H. Yu, P. Jain, P. Kar, and I. Dhillon. Large-scale multi-label learning with missing labels. In ICML, 2014.
[24]
G. Zhu, S. Yan, and Y. Ma. Image tag refinement towards low-rank, content-tag prior and error sparsity. In ACM MM, 2010.
Index Terms
- Subspace Clustering Based Tag Sharing for Inductive Tag Matrix Refinement with Complex Errors
Recommendations
Partial Multi-view Subspace Clustering
MM '18: Proceedings of the 26th ACM international conference on MultimediaFor many real-world multimedia applications, data are often described by multiple views. Therefore, multi-view learning researches are of great significance. Traditional multi-view clustering methods assume that each view has complete data. However, ...
Social image tag enrichment based on textual similarity modeling
In social image sharing websites, users provide several descriptive tags to annotate their shared images. Usually, the user annotated tags are noisy, biased and incomplete. How to improve tag quality is very important for tag based applications. The ...
Tag Completion for Image Retrieval
Many social image search engines are based on keyword/tag matching. This is because tag-based image retrieval (TBIR) is not only efficient but also effective. The performance of TBIR is highly dependent on the availability and quality of manual tags. ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
July 2016
1296 pages
ISBN:9781450340694
DOI:10.1145/2911451
- General Chairs:
- Raffaele Perego,
- Fabrizio Sebastiani,
- Program Chairs:
- Javed Aslam,
- Ian Ruthven,
- Justin Zobel
Copyright © 2016 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]
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 07 July 2016
Check for updates
Author Tags
Qualifiers
- Short-paper
Funding Sources
- National Natural Science Foundation (NSF) of China
- National Basic Research Program of China (973 Program)
- Microsoft Research Asia Collaborative Research Program
Conference
SIGIR '16
Sponsor:
SIGIR '16: The 39th International ACM SIGIR conference on research and development in Information Retrieval
July 17 - 21, 2016
Pisa, Italy
Acceptance Rates
SIGIR '16 Paper Acceptance Rate 62 of 341 submissions, 18%;
Overall Acceptance Rate 792 of 3,983 submissions, 20%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 164Total Downloads
- Downloads (Last 12 months)1
- Downloads (Last 6 weeks)0
Reflects downloads up to 23 Sep 2024
Other Metrics
Citations
Cited By
View all- Li ZTang J(2022)A survey on social image semantic analysisChinese Science Bulletin10.1360/TB-2022-093868:25(3368-3384)Online publication date: 11-Nov-2022
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in