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.
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Index Terms
- Subspace Clustering Based Tag Sharing for Inductive Tag Matrix Refinement with Complex Errors
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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.
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Publication History
Published: 07 July 2016
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Author Tags
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- 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
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SIGIR '16
Sponsor:
SIGIR '16: The 39th International ACM SIGIR conference on research and development in Information Retrieval
July 17 - 21, 2016
Pisa, Italy
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SIGIR '16 Paper Acceptance Rate 62 of 341 submissions, 18%;
Overall Acceptance Rate 792 of 3,983 submissions, 20%
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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
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