skip to main content
10.1145/1101149.1101242acmconferencesArticle/Chapter ViewAbstractPublication PagesmmConference Proceedingsconference-collections
Article

To learn representativeness of video frames

Published: 06 November 2005 Publication History

Abstract

With the rapid explosion of video data, compact representation of videos is becoming more and more desirable for efficient browsing and communication, which leads to a number of research works on video summarization in recent years. Among these works, summaries based on a set of still frames are frequently studied and applied due to its high compactness. However, the representativeness of the selected frames, which are taken as the compact representation of the video or video segment, has not been well studied. It is observed that frame representativeness is highly related to the following elements: image quality, user attention measure, visual details, and displaying duration. It is also observed that users have similar tendency in selecting the most representative frame for a certain video segment. In this paper, we developed a method to examine and evaluate the representativeness of video frames based on learning users' perceptive evaluations.

References

[1]
R. Collobert, S. Bengio, and J. Marithoz. Torch Lib for GMM and EM. http://www.torch.ch/, 2005.]]
[2]
M. A. T. Figueiredo, J. M. N. Leitão, and A. K. Jain. On Fitting Mixture Models. In EMMCVPR, pages 54--69, 1999.]]
[3]
A. Hanjalic. Shot-boundary detection: unraveled and resolved? IEEE Trans. Circuits Syst. Video Techn., 12(2):90--105, 2002.]]
[4]
A. Hanjalic and H. Zhang. Optimal shot boundary detection based on robust statistical models. In ICMCS '99: Proceedings of the IEEE International Conference on Multimedia Computing and Systems Volume II-Volume 2, page 710, Washington, DC, USA, 1999. IEEE Computer Society.]]
[5]
X. S. HUA, L. LU, and H. J. ZHANG. AVE: automated home video editing. In MULTIMEDIA '03: Proceedings of the eleventh ACM international conference on Multimedia, pages 490--497, New York, NY, USA, 2003. ACM Press.]]
[6]
J. G. Kim, H. S. Chang, J. Kim, and H.-M. Kim. Efficient camera motion characterization for MPEG video indexing. In IEEE Intl. Conf. on Multimedia and Expo, volume 2, pages 1171--1174, 2000.]]
[7]
X. Li. Blind Image Quality Assessment. In Image Processing. 2002.International Conference on, pages 449--452. IEEE, 2002.]]
[8]
Y. F. Ma, L. Lu, H. J. Zhang, and M. Li. A user attention model for video summarization. In MULTIMEDIA '02: Proceedings of the tenth ACM international conference on Multimedia, pages 533--542, New York, NY, USA, 2002. ACM Press.]]
[9]
T. Mei, X. S. HUA, H. Q. ZHOU. Tracking Users' Capture Intention: A Novel Complement View for Home Video Content Analysis. In MULTIMEDIA '05: Proceedings of the eleventh ACM international conference on Multimedia, New York, NY, USA, 2005. ACM Press.]]
[10]
A. W. Moore. Clustering with Gaussian Mixtures. http://www-2.cs.cmu.edu/~awm/tutorials/gmm.html, 2005.]]
[11]
S. Uchihashi, J. Foote, A. Girgensohn, and J. S. Boreczky. Video Manga: generating semantically meaningful video summaries. In ACM Multimedia (1), pages 383--392, 1999.]]
[12]
Y.Wu, E.Chang, B.Li. Shot transition detection using a perceptual distance function In IEEE Intl. Conf. on Multimedia and Expo, volume 1, pages 293 - 296, 2002.]]
[13]
P. Yap and P. Raveendran. Image focus measure based on Chebyshev moments. In Vision, Image and Signal Processing, IEE Proceedings, volume 151, pages 128--136, 2004.]]

Cited By

View all
  • (2023)Generating Personalized Summaries of Day Long Egocentric VideosIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2021.311807745:6(6832-6845)Online publication date: 1-Jun-2023
  • (2022)Multimodal Learning toward Micro-Video UnderstandingundefinedOnline publication date: 12-Mar-2022
  • (2019)Multimodal Learning toward Micro-Video UnderstandingSynthesis Lectures on Image, Video, and Multimedia Processing10.2200/S00938ED1V01Y201907IVM0209:4(1-186)Online publication date: 17-Sep-2019
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
MULTIMEDIA '05: Proceedings of the 13th annual ACM international conference on Multimedia
November 2005
1110 pages
ISBN:1595930442
DOI:10.1145/1101149
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: 06 November 2005

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. key-frame selection
  2. quality assessment
  3. representative frame
  4. representativeness
  5. video content analysis

Qualifiers

  • Article

Conference

MM05

Acceptance Rates

MULTIMEDIA '05 Paper Acceptance Rate 49 of 312 submissions, 16%;
Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)1
  • Downloads (Last 6 weeks)0
Reflects downloads up to 16 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2023)Generating Personalized Summaries of Day Long Egocentric VideosIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2021.311807745:6(6832-6845)Online publication date: 1-Jun-2023
  • (2022)Multimodal Learning toward Micro-Video UnderstandingundefinedOnline publication date: 12-Mar-2022
  • (2019)Multimodal Learning toward Micro-Video UnderstandingSynthesis Lectures on Image, Video, and Multimedia Processing10.2200/S00938ED1V01Y201907IVM0209:4(1-186)Online publication date: 17-Sep-2019
  • (2019)Sentence Specified Dynamic Video Thumbnail GenerationProceedings of the 27th ACM International Conference on Multimedia10.1145/3343031.3350985(2332-2340)Online publication date: 15-Oct-2019
  • (2019)Video Summarization by Learning Deep Side Semantic EmbeddingIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2017.277124729:1(226-237)Online publication date: 1-Jan-2019
  • (2018)Video Content StructureEncyclopedia of Database Systems10.1007/978-1-4614-8265-9_1020(4393-4399)Online publication date: 7-Dec-2018
  • (2017)Recognizing and Presenting the Storytelling Video Structure With Deep Multimodal NetworksIEEE Transactions on Multimedia10.1109/TMM.2016.264487219:5(955-968)Online publication date: 1-May-2017
  • (2017)An auto-encoder-based summarization algorithm for unstructured videosMultimedia Tools and Applications10.1007/s11042-017-4485-476:23(25039-25056)Online publication date: 1-Dec-2017
  • (2017)Multi-modal tag localization for mobile video searchMultimedia Systems10.1007/s00530-016-0506-923:6(713-724)Online publication date: 1-Nov-2017
  • (2017)Automatic Image Cropping and Selection Using Saliency: An Application to Historical ManuscriptsDigital Libraries and Multimedia Archives10.1007/978-3-319-73165-0_17(169-179)Online publication date: 21-Dec-2017
  • Show More Cited By

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