Nothing Special   »   [go: up one dir, main page]

skip to main content
10.1145/2245276.2245287acmconferencesArticle/Chapter ViewAbstractPublication PagessacConference Proceedingsconference-collections
research-article

Rushes video summarization based on spatio-temporal features

Published: 26 March 2012 Publication History

Abstract

The film making industry, together with ordinary-home users, are producing a record number of multimedia videos, generating a great demand for new methods to explore the content available in these videos. Here we focus in one methods for automatic rushes video summarization. Rushes consist of unedited material generated during the recording of a video film, and have characteristics not always found in standard videos: a high number of repetitions and a great number of the so called junk shots. To solve this problem, we propose an approach based on spatial and spatial-temporal features represented by a bags of visual features. This representation is robust to a series of transformations in image and occlusion. The task is modeled as an optimization problem, and a strategy inspired by the multiview learning technique is applied. Results on the BBC Rushes database were compared with the three best methods submitted to the TRECVID 2007, and showed the methodology to be promising for dynamic rushes video summarization.

References

[1]
THU-ICRC at rush summarization of TRECVID 2007. In Proc. of the Int. Workshop on TRECVID video summarization. ACM, 2007.
[2]
T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein. Introduction to Algorithms. The MIT Press, 2001.
[3]
G. Csurka, C. R. Dance, L. Fan, J. Willamowski, and C. Bray. Visual categorization with bags of keypoints. In In Workshop on Statistical Learning in Computer Vision, ECCV, pages 1--22, 2004.
[4]
M. Detyniecki and C. Marsala. Video rushes summarization by adaptive acceleration and stacking of shots. In Proc. of the Int. Workshop on TRECVID video summarization. ACM, 2007.
[5]
E. Dumont and B. Mérialdo. Rushes video summarization and evaluation. Multimedia Tools Appl., 48: 51--68, May 2010.
[6]
R. Jain. The Art of Computer Systems Performance Analysis: Techniques for Experimental Design, Measurement, Simulation, and Modeling. John Wiley and Sons, Inc., 1991.
[7]
I. Laptev. On space-time interest points. Int. J. Comput. Vision, 64: 107--123, September 2005.
[8]
D.-D. Le and S. Satoh. National institute of informatics, japan at trecvid 2007: Bbc rushes summarization. In Proc. of the Int. Workshop on TRECVID video summarization, pages 70--73, 2007.
[9]
D. G. Lowe. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision, 60: 91--110, November 2004.
[10]
I. Muslea, S. Minton, and C. A. Knoblock. Active + semi-supervised learning = robust multi-view learning. In Proc. of the 19th Int. Conf. on Machine Learning, pages 435--442, 2002.
[11]
P. Over, A. F. Smeaton, and P. Kelly. The trecvid 2007 bbc rushes summarization evaluation pilot. In Proc. of the Int. Workshop on TRECVID video summarization, pages 1--15, 2007.
[12]
C.-M. Pan, Y.-Y. Chuang, and W. H. Hsu. Ntu trecvid-2007 fast rushes summarization system. In Proc. of the Int. Workshop on TRECVID video summarization, pages 74--78, 2007.
[13]
T. Tuytelaars and K. Mikolajczyk. Local invariant feature detectors: a survey. Found. Trends. Comput. Graph. Vis., 3: 177--280, July 2008.
[14]
K. E. A. van de Sande, T. Gevers, and C. G. M. Snoek. Evaluating color descriptors for object and scene recognition. IEEE Trans. on Pattern Analysis and Machine Intelligence, 32(9): 1582--1596, 2010.

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences
SAC '12: Proceedings of the 27th Annual ACM Symposium on Applied Computing
March 2012
2179 pages
ISBN:9781450308571
DOI:10.1145/2245276
  • Conference Chairs:
  • Sascha Ossowski,
  • Paola Lecca
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: 26 March 2012

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. clustering
  2. dynamic video summarization
  3. rushes videos

Qualifiers

  • Research-article

Conference

SAC 2012
Sponsor:
SAC 2012: ACM Symposium on Applied Computing
March 26 - 30, 2012
Trento, Italy

Acceptance Rates

SAC '12 Paper Acceptance Rate 270 of 1,056 submissions, 26%;
Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 140
    Total Downloads
  • Downloads (Last 12 months)2
  • Downloads (Last 6 weeks)1
Reflects downloads up to 12 Nov 2024

Other Metrics

Citations

View Options

Get Access

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media