ACM Home Page
Please provide us with feedback. Feedback
The COST292 experimental framework for rushes summarization task in TRECVID 2008
Full text PdfPdf (335 KB)
Source
International Multimedia Conference archive
Proceedings of the 2nd ACM TRECVid Video Summarization Workshop table of contents
Vancouver, British Columbia, Canada
Pages 40-44  
Year of Publication: 2008
ISBN:978-1-60558-309-9
Authors
S. U. Naci  Delft University of Technology, Delft, Netherlands
Uros Damnjanovic  Queen Mary University London, London, England, UK
Boris Mansencal  LABRI UMR CNRS/University of Bordeaux, Talence, France
Jenny Benois-Pineau  LABRI UMR CNRS/University of Bordeaux, Talence, France, France
Christian Kaes  LABRI UMR CNRS/University of Bordeaux, Talence, France
Marzia Corvaglia  University of Brescia, Brescia, Italy
Eliana Rossi  LABRI UMR CNRS/University of Bordeaux, Talence, France and University of Brescia, Italy
Naiara Aginako  VicomTech, San Sebastian, Spain
Sponsors
SIGMULTIMEDIA: ACM Special Interest Group on Multimedia
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 1,   Downloads (12 Months): 41,   Citation Count: 1
Additional Information:

abstract   references   cited by   index terms   collaborative colleagues  

Tools and Actions: Request Permissions Request Permissions    Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/1463563.1463569
What is a DOI?

ABSTRACT

In this paper, the method used for Rushes Summarization task by the COST 292 consortium is reported. The approach proposed this year differs significantly from the one proposed in the previous years because of the introduction of new processing steps, like repetition detection in scenes. The method starts with junk frames removal and follows with clustering and scene detection; then for each scene, repetitions are detected in order to extract once the real scene; the following step consists in face detections (faces are considered semantically relevant) and in pan, tilt and zoom detections (other camera motions are usually related to technical operations in the backstage); finally the summary is extracted.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
Opencv. http://opencvlibrary. sourceforge. net, 2007.
 
2
A. Don, L. Carminati, and J. Benois-Pineau. Detection of visual dialog scenes in video content based on structural and semantic features. In Proc. CBMI'05 Létonie, 2005.
 
3
E. Kasutani and A. Yamada. The mpeg-7 color layout descriptor: a compact image feature description of high-speed image/video segment retrieval. In ICIP 2001 Greece, 2001.
 
4
P. Kraemer, J. Benois-Pineau, and M. Gràcia Pla. Indexing camera motion integrating knowledge of quality of the encoded video. In Proc. SAMT'06 2006.
 
5
M. Meila and J. Shi. Learning segmentation by random walks. In NIPS 2000.
 
6
M. Meila and J. Shi. A random walks view of spectral segmentation, 2001.
7
 
8
X. Qian, G. Liu, and R. Su. Effective fades and flashlight detection based on accumulating histogram difference. IEEE Transactions On Circuits And Systems For Video Technology 16(10), 2001.
 
9


Collaborative Colleagues:
S. U. Naci: colleagues
Uros Damnjanovic: colleagues
Boris Mansencal: colleagues
Jenny Benois-Pineau: colleagues
Christian Kaes: colleagues
Marzia Corvaglia: colleagues
Eliana Rossi: colleagues
Naiara Aginako: colleagues