skip to main content
10.1145/2072298.2072050acmconferencesArticle/Chapter ViewAbstractPublication PagesmmConference Proceedingsconference-collections
short-paper

Video-to-shot tag allocation by weighted sparse group lasso

Published: 28 November 2011 Publication History

Abstract

Traditional shot tagging techniques are focused on learning and propagating the tags at the same level, that is from labeled training shots to the unknown test shots. Due to the lack of sufficient labeled video shots, effective shot tagging remains challenging. By observing that video-level tags are more widely provided, we design a novel approach to propagate video-level tags to the test shots. A weighted sparse group lasso method (WSGL) is proposed for shot reconstruction, which well preserves the structural sparsity to reduce the noise in tag propagation. Meanwhile, it simultaneously considers the spatial-temporal information within the video corpus to enhance the tagging performance. Extensive experiments are conducted on two public video datasets to demonstrate the effectiveness of the proposed method.

References

[1]
J. Friedman, T. Hastie, and R. Tibshirani. A note on the group lasso and a sparse group lasso. ArXiv e-prints, 2010.
[2]
X. Liu, B. Cheng, S. Yan, J. Tang, T.-S. Chua, and H. Jin. Label to region by bi-layer sparsity priors. In ACM Multimedia, pages 115--124, 2009.
[3]
S. Siersdorfer, J. S. Pedro, and M. Sanderson. Automatic video tagging using content redundancy. In ACM SIGIR, pages 395--402, 2009.
[4]
A. Yanagawa, A. C. Loui, J. Luo, S.-F. Chang, D. Ellis, W. Jiang, L. Kennedy, and K. Lee. Kodak consumer video benckmark data set: concept definition and annotation. Columbia University ADVENT Technical Report 246--2008--4, 2008.
[5]
W.-L. Zhao, X. Wu, and C.-W. Ngo. On the annotation of web videos by efficient near-duplicate search. IEEE Transactions on Multimedia, 12(5):448--461, 2010.

Cited By

View all

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
MM '11: Proceedings of the 19th ACM international conference on Multimedia
November 2011
944 pages
ISBN:9781450306164
DOI:10.1145/2072298
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: 28 November 2011

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. structure sparsity
  2. video tagging

Qualifiers

  • Short-paper

Conference

MM '11
Sponsor:
MM '11: ACM Multimedia Conference
November 28 - December 1, 2011
Arizona, Scottsdale, USA

Acceptance Rates

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)2
  • Downloads (Last 6 weeks)0
Reflects downloads up to 17 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2017)Multimodal Video-to-Near-Scene AnnotationIEEE Transactions on Multimedia10.1109/TMM.2016.261442619:2(354-366)Online publication date: 1-Feb-2017
  • (2016)Spatial and temporal scoring for egocentric video summarizationNeurocomputing10.1016/j.neucom.2016.03.083208:C(299-308)Online publication date: 5-Oct-2016
  • (2013)Effective transfer tagging from image to videoACM Transactions on Multimedia Computing, Communications, and Applications10.1145/2457450.24574569:2(1-20)Online publication date: 10-May-2013
  • (2013)Video-to-Shot Tag Propagation by Graph Sparse Group LassoIEEE Transactions on Multimedia10.1109/TMM.2012.223372315:3(633-646)Online publication date: 1-Apr-2013
  • (2013)Multi-View Visual Classification via a Mixed-Norm RegularizerAdvances in Knowledge Discovery and Data Mining10.1007/978-3-642-37453-1_43(520-531)Online publication date: 2013

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media