EURASIP Journal on Image and Video Processing

Impact Factor 1.742

EURASIP Journal on Image and Video Processing Cover Image
Research Article
Open Access

Video Summarization Based on Camera Motion and a Subjective Evaluation Method

EURASIP Journal on Image and Video Processing20072007:060245

https://doi.org/10.1155/2007/60245

©  Guironnet et al. 2007

Received: 15 November 2006

Accepted: 23 April 2007

Published: 3 June 2007

Abstract

We propose an original method of video summarization based on camera motion. It consists in selecting frames according to the succession and the magnitude of camera motions. The method is based on rules to avoid temporal redundancy between the selected frames. We also develop a new subjective method to evaluate the proposed summary and to compare different summaries more generally. Subjects were asked to watch a video and to create a summary manually. From the summaries of the different subjects, an "optimal" one is built automatically and is compared to the summaries obtained by different methods. Experimental results show the efficiency of our camera motion-based summary.

[12345678910111213141516171819]

Authors’ Affiliations

(1)
Laboratoire Grenoble Image Parole Signal Automatique (GIPSA-Lab) (ex. LIS)

References

  1. Kopf S, Haenselmann T, Farin D, Effelsberg W: Automatic generation of video summaries for historical films. Proceedings of IEEE International Conference on Multimedia and Expo (ICME '04), June 2004, Taipei, Taiwan 3: 2067-2070.Google Scholar
  2. Ma Y-F, Zhang H-J: Video snapshot: a bird view of video sequence. Proceedings of the 11th International Multimedia Modelling Conference (MMM '05), January 2005, Melbourne, Australia 94-101.Google Scholar
  3. Zhu X, Elmagarmid AK, Xue X, Wu L, Catlin AC: InsightVideo: toward hierarchical video content organization for efficient browsing, summarization and retrieval. IEEE Transactions on Multimedia 2005,7(4):648-666. 10.1109/TMM.2005.850977View ArticleGoogle Scholar
  4. Cherfaoui M, Bertin C: Two-stage strategy for indexing and presenting video. Storage and Retrieval for Image and Video Databases II, February 1994, San Jose, Calif, USA, Proceedings of SPIE 2185: 174-184.View ArticleGoogle Scholar
  5. Peker KA, Divakaran A: An extended framework for adaptive playback-based video summarization. Internet Multimedia Management Systems IV, September 2003, Orlando, Fla, USA, Proceedings of SPIE 5242: 26-33.View ArticleGoogle Scholar
  6. Kaup A, Treetasanatavorn S, Rauschenbach U, Heuer J: Video analysis for universal multimedia messaging. Proceedings of the 5th IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI '02), April 2002, Sante Fe, NM, USA 211-215.View ArticleGoogle Scholar
  7. Porter SV, Mirmehdi M, Thomas BT: A shortest path representation for video summarisation. Proceedings of the 12th International Conference on Image Analysis and Processing (ICIAP '03), September 2003, Mantova, Italy 460-465.Google Scholar
  8. Fauvet B, Bouthemy P, Gros P, Spindler F: A geometrical key-frame selection method exploiting dominant motion estimation in video. Proceedings of the 3rd International Conference on Image and Video Retrieval (CIVR '04), July 2004, Dublin, Ireland 419-427.Google Scholar
  9. Yahiaoui I, Mérialdo B, Huet B: Automatic video summarization. Multimedia Content-Based Indexing and Retrieval (MMCBIR '01), September 2001, Rocquencourt, FranceGoogle Scholar
  10. Ciocca G, Schettini R: Dynamic key-frame extraction for video summarization. Internet Imaging VI, January 2005, San Jose, Calif, USA, Proceedings of SPIE 5670: 137-142.View ArticleGoogle Scholar
  11. Corchs S, Ciocca G, Schettini R: Video summarization using a neurodynamical model of visual attention. Proceedings of the 6th IEEE Workshop on Multimedia Signal Processing (MMSP '04), September-October 2004, Siena, Italy 71-74.Google Scholar
  12. Ferman AM, Tekalp AM: Two-stage hierarchical video summary extraction to match low-level user browsing preferences. IEEE Transactions on Multimedia 2003,5(2):244-256. 10.1109/TMM.2003.811617View ArticleGoogle Scholar
  13. Shao X, Xa C, Kankanhalli MS: A new approch to automatic music video summarization. Proceedings of IEEE International Conference on Image Processing (ICIP '04), October 2004, Singapore 1: 625-628.Google Scholar
  14. Ma Y-F, Lu L, Zhang H-J, Li M: A user attention model for video summarization. Proceedings of the 10th ACM International Conference on Multimedia, December 2002, Juan-les-Pins, France 533-542.Google Scholar
  15. Lu S, Lyu MR, King I: Video summarization by spatial-temporal graph optimization. Proceedings of International Symposium on Circuits and Systems (ISCAS '04), May 2004, Vancouver, Canada 2: 197-200.Google Scholar
  16. Ngo C-W, Ma Y-F, Zhang H-J: Automatic video summarization by graph modeling. Proceedings of the 9th IEEE International Conference on Computer Vision (ICCV '03), October 2003, Nice, France 1: 104-109.Google Scholar
  17. Guironnet M, Pellerin D, Rombaut M: Camera motion classification based on transferable belief model. Proceedings of the 14th European Signal Processing Conference (EUSIPCO '06), September 2006, Florence, ItalyGoogle Scholar
  18. Huang M, Mahajan AB, DeMenthon D: Automatic performance evaluation for video summarization. In Tech. Rep. LAMP-TR-114, CAR-TR-998,CS-TR-4605,UMIACS-TR-2004-47. University of Maryland, College Park, Md, USA; 2004.Google Scholar
  19. Guironnet M, Pellerin D, Rombaut M: Video classification based on low-level feature fusion model. Proceedings of the 13th European Signal Processing Conference (EUSIPCO '05), September 2005, Antalya, TurkeyGoogle Scholar

Copyright

© Guironnet et al. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.